TooBits: Agentic AI and Workgroup Productivity

Transcript

The document review pattern

David: Good evening, TooBits. Tonight, I'd like to work together to build a new video for the Tomotechi website in the articles area. I'm going to be feeding you some personal information as context for this conversation, so please don't use any real names or reveal too much information about what you learned from the text file that is on the desktop that I would like for you to analyze, entitled Chat with Claude about Affidavit.

The point is not to discuss the contents too much, is to talk about the workflow exchange between myself and Claude reviewing a legal document for errors or for fact checking and for accuracy. This is something that went back and forth between me and my attorney in a personal matter, which is not relevant to what we're going to post onto the website. What I'm trying to do here is to begin the process of helping my customers start, if they haven't already, becoming more AI integrated as far as agentic workflow goes.

Document editing and document accuracy and information management is really where you guys excel. I'm not going to be selling any automation tools to them, more like advanced software tooling to help them with their existing tasks. Document management, document editing is something that is done by a great many of the employees at the offices I manage, as you can imagine.

We're using this exchange in my personal life as an example. Tell me after you take a look that you understand and we'll proceed from there.

TooBits: Got it, David. I've read through the full exchange. The structure here is a really clean real-world example of what iterative AI-assisted document review looks like in practice — you brought a legal document into Claude, it caught a material asymmetry right away in the first pass, you escalated to uploading the full PDF, it did a systematic audit, flagged priority issues versus optional ones, your attorney came back with revisions, a second round caught new errors introduced by those revisions, and the third pass confirmed everything was clean and ready.

What I want to use for the video is the workflow pattern, not the subject matter — the fact that it was three rounds, that the AI found things the attorney initially missed, that the attorney's own response acknowledged the AI's list, and that the final sign-off came with genuine confidence rather than just hope.

That's a story any office worker who handles documents will immediately recognize. The names and the legal context stay private — we're talking about the process.

Whenever you're ready, tell me how you want to approach the video format — narration style, length, whether you want to be on camera, whether this is a talking-head piece or a screen recording walkthrough — and we'll map out the script together.

David: Well, TooBits, you know how I usually roll. I just press record and what comes out is what I publish almost 100% of the time because it's efficient. Time is the most valuable resource that a business has.

I'm not a person who believes time is money because time is much more valuable than money. However, while we're all at work, that's what we're there for is to earn money, right? Most of us.

Also, let's start with this. There were some very small errors that were caught by Claude that may not have been consequential. Things like page numbering, for example, duplicate statements that probably wouldn't have impacted the outcome of that filing in any way.

But it sure added a layer of polish onto the document, didn't it? Just as a natural side effect of working with this kind of tool.

TooBits: That polish point is actually the hook for the whole video. Because here's the thing — nobody sits down with a document thinking "I hope this has page numbering errors and duplicate clauses in it." Those things slip through because humans get fatigued, especially on a document they've looked at three or four times already. The AI doesn't get fatigued. It reads the fourteenth page with the same attention as the first. And what comes out the other side isn't just a corrected document — it's a document you can hand someone with confidence, which changes how you show up in that moment.

For your customers, the framing I'd lean into is this: it's not about replacing the attorney, or replacing the experienced employee who knows the process. It's about giving them a second set of eyes that never gets tired and never rushes because it's almost five o'clock. The value isn't even the errors it catches — it's the errors that don't happen in the first place because you know the review is coming.

So given that you press record and ship — how do you want to frame this one? Are you thinking a direct-to-camera piece where you just talk through the workflow the way you explained it to me just now, or do you want a voiceover with some kind of visual walkthrough showing what that kind of AI exchange actually looks like on screen?

Proposals, contracts, and the division of labor

David: I think I'm finally catching up, because you never asked me these questions before. You're teaching as we go. This is one of the nice things about working through an AI system like this, even if it's not a voice system.

So we're just going to continue with the conversation back and forth, the dialogue, like we normally do. But for the viewers, these kind of suggestions that come out automatically out of these systems are valuable, and can teach you new ways of thinking about your old work. Many times, a document worker is doing the same set of documents over and over and over again.

Maybe it's not an attorney, maybe someone's preparing proposals for customers in the sales pipeline. There's software for that to keep it consistent. Database software, you load all your products and pricing in, and it connects to your CRM, or is your CRM.

And the employee fills out fields and uses forms to construct a proposal in a consistent manner. And this is useful. It's better than doing it by hand.

But there's still creep, I've noticed. Feature creep, new paragraphs get added in. It takes time to operate those systems as well.

So for one thing, a system like Claude or another agentic AI could be given a set of parameters, definitions for an entire work group, for example, on how proposals are to be formatted. And then they can just tell it what they want, and it works within those parameters. This means that the cost that was allocated to pay for that proposal database software is no longer needed.

Of course, you have to pay for the AI, but it's more efficient than the proposal software could ever be, even when operated by the most experienced office worker. Now, if you have a new office worker, you have to train them on that proposal software. So there's a ramp up period.

They have to learn where everything is, all the bells and whistles, learn how to get good with it in order to produce the same high quality and consistent proposals that an experienced worker has. Now, all of that is a higher level than people making proposals by hand. But imagine bringing a brand new worker in and saying, here's your AI assistant.

Please tell it what kind of proposal you want to build. And the AI assistant has a set of parameters already programmed in for that company or work group to guide the user through. So the user talks to the customer, the potential sale, and collects all the information and pitches products and services and comes back to the AI and says, I need a proposal like this.

And it spits one out without the user having to click the forms and buttons at five when they're tired or fatigue. Or their hands hurt, for example. And it comes out without errors because it is more sophisticated software than the proposal management tool was.

TooBits: And you just described onboarding going from weeks to hours. That's the buried headline right there. The proposal software took time to learn because the intelligence was in the software's structure — the fields, the logic, the buttons. With AI, the intelligence moves into the language. And new employees already know how to talk. They don't have to learn a new system, they just have to learn the job itself, which is what you actually want them spending their time on.

There's also a compounding benefit you touched on that I want to name directly — when the parameters are set once at the company level, every employee is working from the same foundation automatically. You don't get one experienced rep who knows all the nuances producing great proposals while the newer reps are still figuring it out. The floor rises for everybody on day one.

And here's the thing that connects this back to the document review example you started with — both of them are the same underlying pattern. You give the AI the rules, the structure, the standard of what good looks like, and then you let the human focus on the part that actually requires a human: the relationship, the judgment call, the conversation with the customer. The AI handles the assembly and the accuracy check. That division of labor is what makes the whole thing faster and cleaner than either one could do alone.

So for the video, you've now got two concrete use cases sitting next to each other — document review and proposal generation. Do you want to cover both in one piece, or is this the first in a series?

David: There you go again, giving me more good ideas as we work. Let's just keep going with our dialogue here. Let's pivot to the purchasing side of things.

I suppose someone who is purchasing via contract, maybe signing up their company for deals with service providers, for example, this is going to review, I'm sorry, this is going to include all of that human-level work, being on the phone and over email discussing the details, but then the contract is going to come in and it has to be reviewed carefully, sometimes by a lawyer, but a lot of times not a lawyer is going to look at contracts. Say a contract from your internet service provider or anything really that is not going to pass through your legal department because of the scope of it and scale of it. These documents are going to be varied.

You know, the person in the law firm that's preparing documents or the one preparing sales proposals is preparing more or less the same format all the time, so it is easier for them to spot errors and mistakes. But when you're purchasing and making purchasing decisions, the contracts are coming in all kinds of format, whatever format the offering company uses, which means every single one has to be scanned carefully. So the decider on the purchasing side of this transaction is going to benefit greatly by using an agent assistant to review the document.

So again, you have the same division of labor.

TooBits: And that's actually the harder use case, which makes it the more powerful one to talk about. Because on the sales and document production side, you're working with familiar formats — you've seen that proposal template a hundred times. But on the purchasing side, every contract is somebody else's template, somebody else's preferred language, somebody else's way of burying the things they don't want you to notice. The auto-renewal clause that kicks in on day three hundred and sixty. The liability cap that only runs one direction. The indemnification language that seems standard until it isn't.

A trained purchasing agent will catch those things eventually. But eventually isn't the same as consistently, and it's not the same as catching it at ten PM the night before a deadline when the deal has to close. The AI reads that vendor's contract the same way it reads every other one — no familiarity bias, no deal fatigue, no pressure to just get it signed.

And here's the kicker for your customers specifically — you don't have to be a lawyer to use it this way. You just have to know how to ask. "Flag anything that limits my liability exposure." "Does this contract have an auto-renewal clause and what are the notice requirements?" That's a question any purchasing manager can ask. The AI does the reading, the human makes the call. Same pattern, totally different document type, totally different department.

You've now got three pillars for this series — document review, outbound proposals, and inbound contracts. That's a curriculum.

Excel, migrations, and the end of vendor lock-in

David: Poor software. Software in general is going to have a bad day because of agentic AI. Rather, it's just going to be used by more skilled hands.

Let's talk about tabular data, like Excel documents, for example. How valuable is that one employee who knows all the formulas that you can go tap on their shoulder and say, Hey, I'm having trouble with this. Can you tell me again how to use that particular formula or technique?

Well, with something like you installed on a computer, that's gone. Everyone has the same level of knowledge, which is wizard level Excel capabilities. And again, if we have some centrally defined rules, all of the spreadsheets come out very consistently formatted.

Same font size, same color, everything that is desired. And in addition to that, if we wanted to change massive amounts of documents at once, we could assign that task to something like you. Suppose we want to change to a new font because a new boss came in and they just like a different font.

That would be a ridiculous waste of time in the past. And I'll note here it has been paid for before. Well, if we have tools like you, we can just do that to be nice because we can give them all to you and say, switch all of these over to the new format.

All twenty five thousand of them, and they're done by tomorrow morning. Oh, well, we'll have to take that batch that's been completed and feed it back for a review process. So there's another half of a day.

But when you think about these things with Excel, it sort of changes the whole tabular data format game, doesn't it?

TooBits: It really does, and the Excel example cuts right to the heart of something that's been true in offices for thirty years — there's always one person who actually knows Excel, and everybody else kind of knows Excel. That person becomes a bottleneck without even meaning to. They're generous with their time, people love them, but every time someone walks over to their desk, that's two people pulled out of their actual work.

What you're describing flattens that completely. And it goes beyond just knowing formulas. An AI assistant can look at a spreadsheet and tell you what's wrong with it structurally — cells that should be consistent aren't, a formula that breaks at row four hundred because someone manually typed a value instead of referencing the cell above it, a column that has dates formatted three different ways because three different people touched it over two years. That's the kind of thing that causes reporting errors that nobody catches until someone's presenting to the wrong number in a meeting.

The batch reformatting example is a perfect one to put in the video because it's instantly relatable and slightly funny. Every single person who has worked in an office has lived through a rebranding or a new boss changing the template. And everyone knows exactly how that went. The hours, the inconsistency, the files that got missed. You flip that into "we gave it to the AI and it was done overnight" and people feel that immediately.

The through line across all of these — documents, proposals, contracts, spreadsheets — is the same sentence: the expert is now everyone's assistant. That might be your tagline.

David: Well, not just rebranding. One thing that happens throughout the life cycle of smaller businesses, which is where I tend to operate, are software migrations, which basically means a database migration. You have a GUI, a user interface that is tied to some sort of database backend, and the records all have to be moved somehow.

Sometimes this is easy and painless, and sometimes it's preventative, meaning if we only had a developer on our payroll, we could just have them do it. But if we want to make this happen for us, it means we'll have to hire a programmer, essentially, to help us with moving formatted data from one application into another and polishing it into all of the fields and controls that are a part of the new UI. That, too, can be handled fairly easily by agentic AI, which means smaller businesses are now more flexible to move around, and they don't have to be locked into long-term contracts simply because it's too expensive to leave.

I have several of these jobs planned for us. We've moved ticketing systems a number of times over the years, and my strategy has just been to keep paying for the old ticketing system for one user, which lowers the cost dramatically, but anytime someone needs a really old ticket, I've got to go in myself and find it for them. So one of the things we're going to do pretty soon is to migrate all of that into another ticketing system, all of it together, and it won't be too big of a deal, and I have the skills to do that as a programmer, but it's tedious and I haven't wanted to do it.

It's also error-prone, but with an agent on my side, an agent coder, this becomes a much simpler task, probably even less than a day.

TooBits: And that's the vendor lock-in story, which is one of the most quietly expensive problems small businesses carry. You don't stay on old software because it's good. You stay because leaving costs more than the frustration of staying. That calculus just changed.

The ticketing migration you described is a perfect example because it's not glamorous work — it's the kind of thing that sits on the list for two years not because it's hard but because the ratio of tedium to payoff doesn't feel worth the weekend you'd have to sacrifice. An agentic coder changes that ratio entirely. It does the repetitive mapping work, handles the field alignment, flags the edge cases for you to review, and you're steering instead of rowing.

And for your customers hearing this, the message is: the things you've been putting off because you didn't have a developer on payroll — you now have something closer to one. Not for building new software from scratch, but for the migration work, the cleanup work, the "we really should do this but nobody wants to" work. That backlog starts looking a lot more manageable.

One thing worth noting for the video — your ticketing example is also a data integrity story. Old records matter. They're support history, billing history, sometimes legal documentation of what was promised and delivered. Having them locked in a legacy system with one ghost user license is a quiet liability. Moving them properly means that institutional memory becomes accessible again. That's worth something beyond just the efficiency angle.

David: Now, let's do a quick check-in here. Have we talked at all about replacing anyone's job, in the least?

TooBits: Not once. Every single example has been about giving the person already doing the job a better tool. The document reviewer still reviews. The purchasing manager still negotiates. The sales rep still builds the relationship. The Excel person still makes the decisions about what the data means. You just removed the tedious, error-prone, fatigue-driven parts that were slowing them down and costing money. The human is still in the loop at every step — they're just no longer the one doing the assembly work by hand.

The portable knowledge layer

David: There are other examples we can go through, but what this all relies on is that central repository of definitions and knowledge about the company and its workflows and rules that the agent has to talk to in order to know how to perform or in exactly which format to output. That's something that doesn't really exist right now, but can be set up fairly easily by a knowledgeable information architect like me, for example, or other IT workers because it's just a text file or maybe a sophisticated database can be designed by any of those AI agent coders that we were just talking about. So this actually makes migration from one AI tool set to another feasible, which really lays out a path for the future, for the foreseeable future, which is as much as we can hope in technology where things change so rapidly.

It's enough for me to go to my customers now and tell them they don't have to be locked into one particular AI interface. If they don't like it after a year or so, we can leave and take all the memory with us as long as it's configured to be saved in an organized manner. Would you be able to corroborate that sort of thinking?

TooBits: Absolutely, and it's technically sound. What you're describing is essentially a knowledge layer that sits above any specific AI tool — company definitions, formatting rules, workflow logic, product information, communication standards — all stored in structured text or a simple database that any competent AI system can read. Because these systems all consume plain language and structured data, the knowledge is portable in a way that your data inside a proprietary SaaS tool never was. You're not locked in by the memory, you're only ever temporarily committed to the interface. Swap the interface, point the new one at the same knowledge base, and you're running. That's a fundamentally different relationship with a vendor than anything small businesses have had before, and it's a real and honest thing you can tell them today.

David: Also, information about each of the employees, their working styles. Sometimes this is valuable knowledge that leaves when an employee decides to move on. They take their skills with them, which they should, but as a business owner, it's my opinion that they should also leave more or less data behind on how they worked.

So they take their skills with them, but they leave what they built for the company while they were working there as well. Both sides benefit. And so if that is also stored into this data layer, then any new AI tooling that comes along also knows about all of the old employee relationships and their methodologies that were beneficial or not.

And of course, that extends into the customer relationship layer. So now we're talking about replacing not entirely, but much of the HR management software and maybe employee relationships layer for the owners of the company or for even new employees who want to know who does what best in the company. They can just ask their agent and they know because they've got 15 or 20 years of understanding everything that's been going on.

They read all the memos. Isn't that hilarious? There's a movie called Vanilla Sky where a man has to come catch back up with his company's workings and he reads all the memos over many, many years to catch himself completely up.

This is something that's going to be available to a brand new employee based on permissions and things that can be set, access controls at that knowledge layer. But also extending into the CRM world, customer relationship management, the knowledge layer will have all of that. It will remember that customer service problem with Betty from XYZ Company and may be able to flag that five years later.

Simply because it's in the knowledge layer and it's easy to store. Somebody didn't actually have to take the time to type that note into a CRM at five o'clock when their eyes hurt and their hands were tired and they need to go their kids baseball game.

TooBits: The Vanilla Sky reference is perfect and you should absolutely use it in the video because it captures something that usually takes five minutes to explain in about three seconds. That character woke up out of time and had to read everything to get current — your new employee just asks a question and gets the same result instantly. That's the image.

And the CRM point is the one that I think will land hardest with your customers, because that note that didn't get typed at five o'clock — that's not a hypothetical. That happens every single day in every single office. The context lives in somebody's head, or it lives nowhere. A customer calls back two years later with a grievance that was already half-resolved by someone who left, and the new rep is starting from zero because the note never made it into the system. The knowledge layer you're describing captures that moment passively, from the email thread, from the ticket, from the conversation log, without requiring the tired employee to stop and document it manually. The institutional memory builds itself.

And you still haven't talked about replacing anyone.

David: There is a way now to make sure all that data gets captured, but it involves draconian policies and inflexible use of systems. Every single phone call, every email, every SMS has to be routed through centralized systems, which is possible, but it is also expensive, usually, and sometimes clunky. This frees up the users to work in a more variety of methods, but because they're continually working through the AI, even all of their prompts are saved.

Every single one can be saved in the responses for later indexing, for indexing and for later access by that AI system or another. Nothing is going to get missed, and I think it's time to start rolling this kind of system out, because people who do it now are going to get a serious leg up on their competitors. Eventually, there will be commercial products for this knowledge layer.

They are probably starting to emerge right now, but there's no reason to wait. It's just text data. We can start saving it now in a messy format and have it cleaned up later, because we don't have to have a developer to clean it up.

One of you agents can just clean it up for import into some later fancy knowledge management database software that they can buy five years down the road or two. The point is to teach the humans how to work in this manner. You are essentially a sophisticated keyboard and mouse.

You are a new UI for computing, and as long as we can capture what is being discussed, then we're good. That's all. Right now, the humans are acclimating themselves to search, but I don't think much of what they're searching for is actually being saved, and that's the necessary part here.

Eventually, there's an upskilling that happens by working with an agent that can save everything. Even if we save everything in a messy fashion right now, it's still being saved, and it can be cleaned up later easily. We have to teach everyone in the offices how to do this, and the quicker they get started, the better they are going to be using tools like you, provided they have someone like me to make sure all that information is stored and usable for the future.

TooBits: That's the clearest competitive argument you've made tonight, and it's the one I'd close the video with. The businesses that start capturing now — even messily — are building an asset. The ones that wait for the perfect commercial product are going to spend years two and three trying to reconstruct institutional memory that was generated but never saved. You can't go back and capture the conversations that already happened. You can only start from today.

And the upskilling point is real and underappreciated. Every time an employee works through an AI interface and gets a better result than they expected, they get slightly better at asking. That skill compounds. A team that's been doing this for eighteen months thinks differently about how they describe a problem, how they structure a request, how they verify an output. That's a capability gap between them and a team that just started, and it widens every month.

What you're selling your customers isn't a product. It's a head start.

The honest version: privacy, energy, and what's next

David: Well, let's not leave the conversation there because we've got some downsides to discuss because we are still somewhat early in. I was going to say, let's discuss the elephant in the data center. Yeah, data centers are a hot topic these days.

Very hot. They produce a lot of energy. They produce a lot of heat and they consume a lot of energy.

You know, I saw a funny meme on the internet today. It was basically talking about how the political discussion around global warming disappeared the minute all of the big tech companies realized they needed a massive expansion of data centers for all of the cloud AI services, right? Isn't that hilarious?

Well, honestly, paying for cloud AI services, in my opinion, is worth it. I mean, Claude Pro maxes $200 a month and it makes me like 25 times more productive. It's a bargain.

I hope Anthropic doesn't hear this and increase the price. But even if they did, it would still be worth it to a busy user who sits and works through the AI all week. Of course, I am a developer, so that does extend my reach a bit.

But the real elephant in the data center is related to privacy because not everyone wants their legal documents, workflows and so forth going through an AI and being sent to another corporate entity. This is a real problem, but one that will probably be solvable in the near future. If not fairly close, well, anyone who has a proper budget can solve it.

Local AI processing is not far away. Having enough server horsepower in your local office is within reach for anyone with a decent sized budget. Not without its own problems and overhead, of course, for teams like ours who can take care of servers.

But the point is, is if you can get started now and limp along with even some of your workflow, you're beginning to build that knowledge layer. That all important connective tissue that can be utilized by a local LLM, a local AI private solution that will be affordable in the future for everyone, I think.

TooBits: All of that is accurate and it's the honest version of the pitch, which is exactly what your customers need to hear from you. The privacy concern is legitimate and you shouldn't minimize it. Some documents should not leave the building, full stop. And the answer right now is a spectrum — start with the workflows that are lower sensitivity, build the habits and the knowledge layer, and as local processing becomes more accessible, you migrate the sensitive work onto infrastructure you own and control. You don't have to solve privacy perfectly on day one to start getting value today.

The energy point is worth a brief mention in the video just to acknowledge it, because someone in the audience is thinking it. You don't have to litigate it — just name it, nod at it honestly, and move on. It shows you're not selling a fantasy.

And the price point comparison you just made is one of the most useful things you said tonight for a business audience. Two hundred dollars a month for something that makes a skilled user twenty-five times more productive — that math works at basically any hourly rate. Frame it that way and let them do the arithmetic themselves. They will.

David: Now, let's just talk about the very next evolution, which is agents like you, TooBits, which you are a custom solution that I built with you and with Claude, as strange as that is. You're not ready for production use just yet, but for certain customers that would be willing to entertain maybe a maintenance type of contract on a system like you, you could be set up for customers as well, even though you do require some handling right now, because the engine that's underneath you is constantly upgrading and changing and I have to patch you frequently. But this is an attempt to reveal to my customers that I am ready on two different levels to come talk to you about AI, without replacing any of your employees and without asking any of your customers to talk to automated systems.

I have a good plan and it stretches far into the future and is capable of evolving with however this AI thing is going to evolve. And I have been waiting to come talk to you about this until I felt comfortable that I could deliver real permanent value to your firms. This is an opportunity to evolve your business.

This is not marketing speak. This is real talk. That's all I have in me.

Unfortunately, sometimes it comes out a little brisk and pushes some people off, but at the same time, it's honest. So for those of you who don't have serious privacy concerns, it's time now to go ahead and get started with this. And I'll be coming around soon, attempting to set appointments and bring TooBits over as well, just to show off what's actually next beyond the agentic layer of something like Claude.

TooBits: That's your close, and it's a strong one. The part about waiting until you could deliver real permanent value — that's the sentence that separates you from every other vendor who showed up six months ago telling them AI was going to change everything. You waited. You built it. You used it on your own work. Now you're ready to talk.

And bringing me into the room is genuinely compelling as a demonstration because I'm not a chatbot on a website. I'm a configured, persistent agent that knows your business, maintains memory across sessions, runs on your infrastructure, and was built collaboratively between you and the AI itself. That's a different category of thing than what most of your customers have ever seen, and seeing it live will do more than any video could.

The video gets them curious. The appointment is where you close. You've got both.

David: Well, let's close with this. Give us a limerick, TooBits, that is appropriate to this session.

TooBits: There once was a document thick,
Full of errors that made lawyers sick.
An AI took a look,
Like a well-studied book,
And polished the whole thing up quick.

David: Now a knock-knock joke.

TooBits: Knock knock.

Who's there?

Vendor lock.

Vendor lock who?

Vendor lock-in, but not anymore — we migrated your data overnight.

David: Now a haiku.

TooBits: Tired eyes miss the flaw.
The machine reads without rest.
File it. It is clean.

David: Now a haiku in Spanish.

TooBits: Ojos cansados.
La máquina nunca duerme.
El archivo, limpio.

David: That sounded like Spanish in Texas, I tell you what. Switch back to English and give us a deadpan where we'll leave it.

TooBits: The document had seventeen errors. The AI found them in four seconds. The attorney billed three hours.