AI / LLM Integration Services

What is AI integration at TPC?

AI integration is the work of turning a model API into a feature that customers rely on. That includes prompt and context design, tool-use surfaces, retrieval-augmented generation against your own corpus, structured output schemas, streaming UX, latency and cost budgets, observability for non-deterministic systems, and an evaluation set that gives the team confidence to ship changes. The model is the easy part; the hard part is the system around it.

We build first-class agentic systems — Claude or GPT loops with structured tool calls, MCP servers exposing your internal capabilities, multi-step task graphs with retry and fallback, and human-in-the-loop checkpoints where the stakes warrant them.

When you need this

Hire us for AI integration when you have a real workflow that LLMs can collapse — customer support triage, internal search over years of documents, multi-step research, code generation against your own SDK, structured extraction from messy inputs, or an in-product assistant that does more than paraphrase a help article. We are also a good fit when you have already prototyped something in a notebook and need a team to make it a production feature with evals, monitoring, and a credible cost model.

We are not the right team if you want a generic chatbot grafted onto a landing page, or a system that we are asked to certify as “safe” without giving us the ability to constrain its tools and scope.

Our approach

We start with an evaluation set before we write production prompts. Twenty to a hundred labeled examples of the real task, scored against rubric criteria, give us a baseline number we can move. From there we iterate prompts, retrieval strategy, tool surfaces, and model choice against that fixed harness — and we never ship a change that regresses the score without an explicit decision.

We treat tool-use definitions as API contracts. Each tool gets a name, a typed schema, a description tuned for the model, an idempotency story, and a clear failure mode. We expose those tools via MCP servers when the system spans multiple clients, and via direct in-process functions when it does not.

Tech stack we use

Models: OpenAI (GPT-4.1, GPT-5, o-series reasoning models), Anthropic Claude (Opus, Sonnet, Haiku) with extended thinking, prompt caching, and tool use.
Orchestration: LangChain, LangGraph, Vercel AI SDK, and hand-rolled state machines where the framework gets in the way.
Tooling protocol: Model Context Protocol (MCP) servers in TypeScript or Python; stdio and HTTP transports.
Retrieval: pgvector, Pinecone, Turbopuffer, or Cloudflare Vectorize; hybrid BM25 + dense retrieval with rerankers (Cohere, Voyage) where precision matters.
Evals & ops: Promptfoo, Braintrust, LangSmith, OpenTelemetry traces, and a homegrown harness for golden-set regression testing in CI.
Fine-tuning: OpenAI fine-tuning, LoRA on open models via together.ai or Modal when first-party fine-tuning is the right answer (which is rarely).

Typical engagement

A typical AI integration engagement runs six to sixteen weeks. The first two weeks produce an eval harness and a working prototype against real data. Weeks three through eight ship the production feature — UI, observability, cost controls, fallbacks, and a documented runbook for when something regresses. Beyond that we either move to a retainer for ongoing prompt and eval work or hand off to your team with a written playbook.

Examples of work

Recent AI work includes a Claude-based agent that automates a multi-step research workflow with seven custom tools and a verification step, an MCP server exposing a customer's internal API surface to Claude Desktop and ChatGPT, and a RAG system over a six-million-token corpus with hybrid retrieval and a domain-tuned reranker.

What we don't do: pure ML research, training foundation models from scratch, generic “AI strategy” consulting unattached to code, or agents we are not allowed to constrain.