AI integration that survives contact with production
Most AI projects stall between the demo and the deployment. We build the part that runs every day — the ingestion, the retrieval, the error handling, the monitoring — so the system still works in month six.
The gap between a demo and a system
An AI demo takes an afternoon. An AI system that a business depends on takes considerably longer, and almost none of the extra time goes into the model.
It goes into the parts nobody demos: getting at data that lives in five systems with three access patterns, handling the documents that are scanned sideways, deciding what happens when the model is confident and wrong, and building the monitoring that tells you quality has drifted before a customer does.
That is the work we do. We integrate AI into operations that already exist, under the constraints those operations already have.
What we build
Document and intake pipelines. Systems that ingest high volumes of unstructured documents — contracts, forms, invoices, correspondence — extract what matters, and route it where it needs to go. Event-driven, so throughput scales with load instead of a nightly batch window.
Retrieval systems. Search and question-answering grounded in your own content, using retrieval-augmented generation so answers cite real sources instead of inventing them. This is the difference between a system your team trusts and one they quietly stop using.
Workflow automation. The repetitive judgment work that currently occupies a person for hours a week: triage, classification, summarization, routing, first-draft generation. Automated where automation is reliable, escalated to a human where it is not.
Agentic systems. Multi-step processes where the system decides what to do next — carefully scoped, with explicit boundaries on what actions it can take. Useful when the process genuinely varies. Overkill when it does not, and we will say so.
AI strategy. Sometimes the most valuable engagement is a short one that establishes what is worth building, what is not yet feasible, and what your data would need to look like first.
How we decide whether AI belongs
Hiring an AI consultant should mean getting someone who will tell you when the answer is not AI. We start from the problem, not the technology. The questions that matter are whether the task has a clear definition of correct, whether you have enough data to ground it, what happens when the system is wrong, and whether a simpler approach gets most of the value.
A meaningful share of the problems brought to us as AI problems turn out to be integration or reporting problems. Those are cheaper to solve, more reliable in production, and easier to maintain. We would rather build that and be right than build the impressive thing and watch it get switched off.
When AI does earn its place — genuinely unstructured input, genuine language understanding, genuine scale — it is transformative. The judgment is in knowing which situation you are in.
The stack
We are not tied to one vendor, and the right choice depends on the workload.
- Models: Anthropic Claude, OpenAI, and Google Gemini, selected per task on cost, latency, and quality rather than habit
- Retrieval: vector search over Postgres with
pgvector, or a dedicated vector store where scale justifies it - Backend: Python and TypeScript on Node, event-driven with proper queueing so ingestion spikes do not become outages
- Data: Postgres, BigQuery, and object storage, with pipelines that are re-runnable and idempotent
- Cloud: AWS, Google Cloud, and Cloudflare
- Application layer: React and TypeScript for the interfaces your team actually uses
- Operations: structured logging, tracing, evaluation suites, and alerting — because an AI system without observability is a system you cannot debug
How an engagement works
Every engagement starts with a free discovery call — a conversation about your business and constraints, not a proposal. From there we produce a written scope: what we are building, why that way, what is explicitly out of scope, timeline, and cost. If the scope changes, we say so before changing course.
Delivery runs in defined phases with a check-in at each milestone, so you see progress in working software rather than status updates. At handoff you get documentation written for a human being, a walkthrough of what was built and why, and a clear picture of what it takes to maintain.
We stay reachable afterward. In most cases we are the ones maintaining it.
Common questions
What does an AI integration project actually involve?
Usually less model work than people expect and more plumbing than they hope. A typical engagement is data access and cleanup, a retrieval layer, prompt and evaluation design, the application surface your team uses, and the monitoring that tells you when quality drifts. The model is often the smallest decision.
Do I need my own AI model, or can we use an existing one?
Almost always an existing one. Commercial models from Anthropic, OpenAI, and Google are strong enough for the overwhelming majority of business problems, and fine-tuning or self-hosting adds cost and maintenance most companies do not need. We recommend training only when there is a measurable reason.
How do you keep an LLM from producing wrong answers?
By constraining what it is allowed to answer from. Retrieval-augmented generation grounds responses in your actual documents, structured outputs keep responses machine-checkable, and evaluation suites catch regressions before your users do. You also need a path for a human to intervene — systems that cannot be corrected do not stay trusted.
What does AI integration cost?
Scoped per project after a discovery call, because a document pipeline over a clean data source and one over twenty years of scanned PDFs are not the same job. You get a written scope with the cost, timeline, and what is explicitly excluded before any work starts.
How long before we see something working?
A narrow, working slice usually lands in the first few weeks — one real workflow, end to end, with real data. That is deliberate. It is the fastest way to find out whether the approach is right before committing to the full build.
What if AI is not the right answer for my problem?
We will tell you. A large share of problems described as AI problems are integration problems, reporting problems, or process problems, and they are cheaper and more reliable to solve directly. We would rather build the right smaller thing than the impressive wrong one.
Scope it properly first
Before anything is quoted, we spend thirty minutes on what you're actually trying to build and whether this is the right service for it. Free, and there's no proposal attached.