AI · Service

AI Integration

LLMs, RAG and agents — wired into your product, not bolted on.

We integrate AI features that pass review with real users — not demos. LLM-powered workflows, RAG over your data, autonomous agents with guardrails, evals to keep quality from regressing. Cost and latency tracked from day one.

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What you get

  • ◆LLM feature design (chat, copilot, summarization, classification)
  • ◆RAG with vector search (pgvector, Pinecone, Qdrant, Weaviate)
  • ◆Agent workflows with tool use and guardrails
  • ◆Prompt engineering, prompt versioning, prompt caching
  • ◆Evals: golden sets, regression tests, scoring
  • ◆Cost and latency monitoring, fallback strategies
  • ◆PII redaction, content moderation, audit logs
  • ◆Streaming UI for low-latency feel

Technologies we reach for

OpenAIAnthropic ClaudeGoogle Geminiopen-source models (Llama, Mistral)pgvectorPineconeQdrantLangChainLlamaIndexVercel AI SDKInngestTrigger.dev

How we work

  1. 01DiscoveryUse-case definition, data audit, feasibility prototype, eval criteria.
  2. 02PrototypeWorking slice with real data and real users — ship to learn before you scale.
  3. 03BuildProduction hardening: retries, fallbacks, cost caps, evals, observability.
  4. 04LaunchPhased rollout with feature flags, monitoring on quality, latency and cost.
  5. 05OperateEval suite maintained, prompts versioned, cost optimized as models evolve.

Outcomes

  • ◆AI features users actually use (not abandoned after week two)
  • ◆Cost per request tracked, capped and optimized
  • ◆Quality regressions caught before they ship
  • ◆Compliance-aware: PII handling, audit logs, content moderation

Ideal for

  • ◆Product teams adding their first AI feature
  • ◆SaaS companies adding a copilot or AI assistant
  • ◆Companies with proprietary data needing RAG
  • ◆Teams with a working prototype that needs production hardening

Frequently asked

OpenAI, Anthropic or open-source?

Depends on the task. Anthropic Claude for long context, careful reasoning and safety-sensitive workflows. OpenAI for breadth and tooling. Open-source (Llama, Mistral) for cost-sensitive or self-hosted needs. We benchmark on your data.

Can you build a chatbot over our internal docs?

Yes — that is a typical RAG project. Ingestion pipeline, chunking strategy, vector store, retrieval, citation, evals. Two to six weeks depending on scope.

How do you control AI cost?

Prompt caching, model routing (cheap model for easy turns, capable model for hard ones), response caching, batching, hard budget caps with alerting.