AI Integration & Workflow Automation
Make AI useful inside your product.
We embed LLMs, vector search, and autonomous agents into real products and workflows — with retrieval, evals, guardrails, and the engineering rigor to keep AI safe in production.
AI you can
actually ship.
LLM Integration (GPT, Claude, Llama)
Production GPT, Claude, and open-source LLM integrations with prompt versioning, fallbacks, and per-tenant rate limits.
RAG & Pinecone Vector Search
Retrieval-Augmented Generation pipelines on Pinecone, pgvector, or Weaviate — chunked, embedded, and searchable in seconds.
AI Agents & Workflow Automation
Multi-step AI agents that triage tickets, qualify leads, draft documents, and run scheduled jobs — with full audit logs.
Evals, Guardrails & Observability
Automated evals, content guardrails, prompt-injection defences, and PostHog-style observability for every AI call.
Capabilities
AI engineering, not AI demos.
Retrieval-Augmented Generation
Vector search over your private data with cited answers users actually trust.
- Pinecone & pgvector pipelines
- Document chunking + embeddings
- Reranker (Cohere / BGE)
- Citations & confidence scores
- Latency under 800ms
Function calling & tools
LLMs that call your APIs safely — Stripe, your CRM, internal tools — with retries and structured output validation.
integration_instructionsAI workflow automation
n8n, Make, and custom agent runtimes that automate ops — invoice processing, lead routing, document drafting, and more.
See AI integrationsAI-assisted internal tools
Internal tools that draft, summarise, and review — wrapped in role-based UI with full audit trails.
From prompt
to production.
Most AI work fails in evals, not demos. We engineer for the boring 90% so the product feels magical.
Step 01
Use case scoping
We pick AI use cases with measurable ROI and feasible accuracy — and skip the ones that look magical but break in production.
Step 02
Data + retrieval
Source data, chunking, embeddings, reranking, and citations so answers are grounded in your knowledge base.
Step 03
Evals & guardrails
Automated eval suites, prompt-injection defences, and content moderation so quality does not regress.
Step 04
Ship & observe
Tracing, cost dashboards, and a human-in-the-loop review queue so AI gets safer the longer it runs.
Latest Success
Real outcomes from real client work.
40%
Load Increase
2.4s
Saved on TTI
Frequently asked.
Will my data be used to train models?
No. We default to enterprise endpoints (OpenAI, Anthropic, Bedrock) where data is never used for training, and we sign DPAs to confirm.
How accurate can RAG really be?
For well-bounded domains, 90%+ groundedness is achievable with reranking and good evals. We measure it before we ship.
How do you control AI cost?
We tier models (cheap → premium), cache aggressively, batch where possible, and report per-tenant cost on a real dashboard.
Can you migrate prompts as models evolve?
Yes — versioned prompts, eval suites, and a swap-the-model abstraction let you upgrade without regression.
Ship AI that earns its keep.
Tell us your AI use case. We will respond with a feasibility assessment, an accuracy target, and a fixed-fee plan.