AI implementation consultant
We embed AI engineers in your existing team to build the shared foundation and the systems that run on it. We write the code, wire the integrations, and hand back working AI inside the tools your team already uses. The first system is small on purpose and agreed against a number it has to move.
01 / The role
An AI implementation consultant helps your team take the thing the company has decided AI should do and make it happen in production. We work inside the team: choose the workflow together, get access to the data, write the code, wire it into the systems people already open every morning, and stay until it is being used.
The role sits one step downstream of strategy. Strategy names where AI should go. Implementation puts it there and stays until the team is using it. Most teams start with the tool. We start at the result you are paid for, find the one thing in the way, and build the smallest system that moves it.
In practice the job is unglamorous. Most of it is reading how work actually moves, finding the record that lies, deciding what the AI is allowed to touch, and building the smallest system that changes the number. The part that looks like AI is the short part.
If the workflow is already chosen and you want the delivery scope rather than the operating model, start with our AI implementation services. If the first build has to run a whole workflow across several tools, the better fit is our AI agent development work.
02 / Fit
You can already see what AI should be doing in the business and the company has not caught up. There is a workflow bleeding hours every week, the tools do not talk to each other, and there is nobody senior enough inside to build the fix without dropping everything else.
Nobody can name the workflow yet, in which case AI consulting is the cheaper first step. Or the scope is already specified and your own engineers have the time, in which case you want integration work, not a consultant.
03 / First build
The first build is chosen with the people who run the work, not pulled from a menu. These are three shapes it can take when the underlying problem is already clear.
When client delivery, finance, and ownership data each live in a different place, the first build can be a shared company layer. It gives the existing team one current view to work from, then gives later dashboards and agents the same definitions instead of another disconnected tool.
When to choose this: A fit when the question is not which AI tool to buy, but how to make the records your team already relies on agree.
See the company-view systemWhen important knowledge is trapped in conversations and documents, the first build can collect that context into the shared layer, respect who may see it, and return an answer with its source. Your team keeps the judgment; the system makes the underlying evidence easier to find.
When to choose this: A fit when people are already doing the work but lose time checking which answer or document is current.
See the source-backed answers systemWhen a locked system cannot be replaced, a build can sit around it: read the inputs, reconcile the records, and route the completed work to the person who should make the final call. The existing process remains in charge while the manual cross-checking shrinks.
When to choose this: A fit when the risk is real, but the team is spending its time preparing work for a decision it still needs to make.
See the invoice approval system04 / Choosing one
Five questions worth asking on the first call. Ask them of us as well, and hold the answers to the same standard.
01
Ask who writes the code and which people will work with your team in week three. If the answer is a delivery team you have not met, part of the fee is buying a management layer on top of the build. That can be the right trade on an enterprise budget and rarely is on a smaller one. You should know who will be doing the work with you.
02
The fastest implementation adds nothing for anyone to adopt. If the proposal starts with a new platform, a new dashboard, or a migration, the rollout risk moves from the consultant to your team. Ask what changes for the person who does the work today, and listen for whether the answer is a new login.
03
You should. Ask what happens if the engagement ends in month three: who holds the code, whether it runs on tools you already pay for, and what handover looks like. A consultant whose systems only run while they are being paid has sold you a dependency, not an implementation.
04
Every system needs a line between what the AI drafts and what actually goes out. Ask where that line sits and who moves it. Anywhere a mistake would cost money or reputation, a person should approve first, inside the tool your team already works in.
05
A useful engagement names the metric before the build starts and then shows it in production: response time, hours removed, qualified inquiries, completion rate, error reduction. If nobody will name a number, nobody can be held to one, and the engagement becomes a series of interesting builds nobody can price.
05 / The engagement
A thirty-minute call, then a short written plan: what we would build first, what it needs from you, who approves what, and the number it should move. If the plan is not obviously worth doing, we say so and you keep the plan. From there, six steps.
01
We map how work actually moves through your business: the teams, the tools, the handoffs, and where it slows down.
02
We agree what winning looks like in your numbers: the metric that should move and what moving it is worth.
03
We name what is stopping you getting there today: bottlenecks, manual chains, and data stuck between systems.
04
We architect systems that run whole workflows end to end, from intake to execution to reporting, not single steps.
05
We build on top of your existing systems, no migrations, and train your team to run what we build.
06
We measure performance in production against the goal and keep tuning, so the system improves the longer it runs.
06 / Cost
We price against the specific build, on the call, once we both know what we are solving. Monthly rolling, no annual contract, no setup fee, no minimum term. You can stop whenever it stops being worth it, and we would rather you did than sit on a retainer you have stopped valuing.
There is no setup fee and no account manager layer, because there is not one. The people doing the work are the people working with your team.
07 / Boundaries
08 / Questions buyers ask
Built systems inside your existing tools, not strategy documents. The deliverable is the thing running in production: the inbound qualifier reading your inquiries, the responder drafting replies inside the mailbox your team already uses, the sync layer keeping records straight between systems that never talked. A strategy consultant produces a roadmap. An implementation consultant produces the system.
Strategy helps you choose where AI belongs and which problems are worth solving. Implementation turns that scope into working systems. At TwoHundred, the engineers building with your team stay involved in those decisions, so the plan reflects your data, tools and the people who will use the result.
We price against the specific build, on the call, once we both know what we are solving. Monthly rolling, no annual contract, no setup fee, no minimum term. You can stop whenever it stops being worth it, and we would rather you did than sit on a retainer you have stopped valuing.
The first system is deliberately small so it is running and being used early, not perfect and late. We agree the number it has to move before we start, and you will see it moving or not moving. No long implementation, no change programme.
A full-time lead makes sense once AI work is continuous and there is enough of it to fill a role. Before that point you are paying a salary for capacity you cannot yet use. An embedded engagement gives you the build capacity now, and if you hire later the systems and the documentation behind them are already there to inherit.
No. We work with the team you already have, usually the founder plus whoever actually runs the workflow, and we handle the technical build. If your engineers own part of the stack we are touching, they own it after we leave too.
You own them. We build on tools you are already paying for, so there is no vendor lock-in and no platform we sell you monthly. If you bring in a full-time AI lead later, they inherit running systems and the documentation behind them.
We are. Every system has a human approval step wherever getting it wrong would cost you money or reputation. The AI does the work, your team decides what goes out, and we are on the hook every month to keep it right.
Founder-led companies with a real product and a real team, where the founder can see what AI should be doing and the company has not caught up yet. The industry matters less than that gap. What the good ones have in common is that they want the thing working, not a report about it.
09 / Where this connects
The delivered service itself: workflow audit, integration, rollout, and the first live release.
Use this when the first build has to run a whole workflow across tools rather than one step.
Use this when the first workflow has not been chosen yet and needs narrowing.
The upstream engagement that decides where AI belongs before anything gets built.
The smaller-company version of this engagement, scoped to a leaner team.
The systems we have built, what they were meant to fix, and what changed once they ran.
Thirty minutes. We look at the workflows costing you the most hours or money, work out what we would build first and what it has to move, and tell you whether it is worth paying us to do it. If it is not, we say so.
Book a 30-minute call