Services / AI Implementation
AI that does actual work.
Not a chatbot bolted onto the side of your business — models wired into the systems your team already uses, with the guardrails to trust them.
The problem with most AI projects
Most stalled AI initiatives did not fail because the model was not good enough. They failed because nobody connected the model to the data it needed, nobody defined what a correct answer looked like, and nobody decided what happens when the system is unsure.
A pilot that produces impressive demos and no production usage is the normal outcome, not the unlucky one. The difference between a demo and a system is almost entirely the unglamorous part: access to real data, a measurable definition of success, error handling, and a deliberate human fallback.
That unglamorous part is the work.
What gets built
LLM integration into existing tools
Models wired into the CRM, ticketing system, database, or internal app where the work already happens — so nobody has to copy text into a separate chat window and paste the result back.
Custom agents that take action
Agents with a narrow, well-defined job and real permissions: read these records, apply these rules, write the result back, escalate anything ambiguous. Scoped tightly enough to be predictable.
Retrieval over your own knowledge
Pulling the right context out of your documents, tickets, and records at the moment a decision is made, so answers are grounded in your business rather than the model’s general training.
Evaluation sets and guardrails
A held-out set of real historical cases with known-correct outcomes, so changes can be measured rather than guessed at — plus confidence thresholds that route uncertain cases to a human.
AI implementation, answered
What is AI implementation consulting?
AI implementation consulting is the work of getting a large language model doing a specific, measurable job inside systems a business already runs — as opposed to advising on AI strategy in the abstract. In practice it covers choosing where AI actually helps, wiring models into existing tools and data, building agents that take real actions, and putting evaluation and guardrails around them so output can be trusted in production.
How is this different from buying an AI product?
Off-the-shelf AI products solve generic problems generically. They are usually the right answer for things like transcription, general writing help, or support deflection. Custom implementation is worth it when the value is locked in your own data and processes — the scoring rules specific to your business, the systems only you use, the judgment your team applies that no vendor has modeled.
Where does AI actually pay off in a business?
Consistently: reading high volumes of unstructured text and extracting structure from it; triaging and routing work so humans only see what needs judgment; drafting the first version of repetitive written output; and reconciling messy data across systems. It pays off poorly where accuracy must be perfect and unverifiable, where the task is rare, or where a deterministic script would do the same job more cheaply and predictably.
How do you keep an AI system from producing wrong answers?
Three layers. Constrain the task so the model has a narrow job with the right context retrieved for it rather than open-ended reasoning. Build an evaluation set from real historical examples and measure changes against it instead of judging by vibes. Then design the failure path deliberately — low-confidence cases route to a human, and every decision is logged so errors can be traced and corrected.
Which models and platforms do you build on?
Model choice is a per-task decision, not a religion. Frontier models from Anthropic, OpenAI, and Google each win different tasks, and smaller or self-hosted models often win on cost for narrow, high-volume steps. Systems are built so the model can be swapped without rewriting the surrounding workflow, because the landscape changes faster than most procurement cycles.
How long before we see something working?
A first workflow in production typically takes two to six weeks depending on how accessible the underlying systems are. Integration and data access are almost always the slow part, not the AI. Projects that stall usually stall on credentials, API limits, or an internal decision about data handling.
What does a deliverable look like?
Running code in your repositories and cloud accounts, documentation covering how it works and how to operate it, an evaluation set you can rerun as things change, and monitoring that tells you when it breaks. You own all of it.
Find out whether AI actually helps here
Describe the process you are considering automating. I will tell you whether a model belongs in it, whether a plain script would do the job better, and roughly what building it takes.