AI & LLM Integration

Who this is for

• Products that need AI features grounded in their own data
• Teams with a working prototype that is not reliable enough to ship
• Companies whose LLM costs are scaling faster than usage
• Businesses replacing manual processes with agent workflows

What you get

• Production LLM integration with error and fallback handling
• Retrieval pipeline over your documents or database
• System prompts with structured, schema-validated output
• An evaluation harness so prompt changes can be measured
• Cost and latency instrumentation per request
• Streaming responses with graceful degradation

The model is the easy part

Calling an LLM API takes an afternoon. What takes real work is everything around it: grounding responses in your data so they are correct rather than plausible, validating output against a schema so downstream code can rely on it, handling rate limits and timeouts without breaking the user's flow, and keeping cost proportional to value. A demo that works on the happy path is roughly ten percent of a production integration.

Retrieval-augmented generation, done properly

Most 'the AI is hallucinating' problems are retrieval problems. If chunking splits sentences mid-thought, embeddings retrieve badly and the model is handed irrelevant context — then blamed for the result. Chunk boundaries, overlap, retrieval count and reranking are where accuracy actually comes from, and each is a decision worth making deliberately rather than accepting a tutorial default.

Structured output you can build on

Free-text responses are unusable as input to other code. Constraining the model to a schema, validating every response, and retrying on mismatch turns an LLM from a chat toy into a component you can compose. This is the difference between a feature that works in a demo and one that survives a thousand real users.

Evaluation, so changes are measurable

Without evals, prompt engineering is guesswork with a confident tone — you change wording, it seems better, and you have no idea what it broke. A test set of representative inputs with expected properties makes prompt changes measurable and stops the slow regression that otherwise creeps in over months.

Cost as a design constraint

Token cost is a function of architecture. Retrieval instead of context-stuffing, caching stable prefixes, routing simple requests to smaller models, and streaming so perceived latency stays low regardless of total tokens. These decisions are made at design time; discovering them from an invoice is the expensive path.

How the engagement works

1. 01

   Discovery

   Identify where AI genuinely adds value and, just as importantly, where it does not. Some problems want a database query and a rule, not a language model.

2. 02

   Prompt & guardrails

   Design system prompts, output schemas and refusal behaviour, with an evaluation set built alongside so quality is measurable from the start.

3. 03

   Integrate

   Wire the APIs, retrieval layer and vector index into your application, with cost and latency instrumented per request.

4. 04

   Ship

   Deploy with evaluation, telemetry and cost monitoring in place, so regressions surface before your users report them.

Technology

Models

The OpenAI, Anthropic Claude and Google Gemini APIs — chosen per task rather than by preference, since they differ meaningfully on long-context handling, tool use and cost.

Retrieval

LangChain for pipeline plumbing; Convex, Supabase pgvector or a dedicated vector store depending on index size and filtering needs.

Agents & automation

Agent frameworks, tool calling, MCP, and N8N where a visual workflow is easier for the client to own than code.

Application

Next.js server components and streaming, so credentials stay server-side and responses arrive progressively.

Proof

Frequently asked