AI that earns its keep.
Not demos. Production AI systems — copilots, agents, RAG pipelines and automations — with evals, guardrails and a measurable ROI inside the first quarter.
What we build
Engineered AI features your team trusts in production — with the evals, observability and rollback paths to prove it.
LLM Integrations
Claude, GPT, Gemini and open-weights models wired into your product with prompt versioning, eval suites and cost controls.
RAG & Document AI
Retrieval pipelines over your contracts, manuals and tickets — chunking, embeddings, reranking and citation-grounded answers.
Agentic Workflows
Tool-using agents that triage tickets, reconcile invoices, write reports and call your APIs — with humans in the loop where it matters.
Knowledge Graphs
Entity-resolved graphs over CRM, ERP and unstructured data — Neo4j, GraphRAG and semantic search that actually understands context.
Internal Copilots
Domain copilots embedded in Slack, Teams, your admin console — trained on your playbooks, scoped to your permissions.
Workflow Automation
n8n, Temporal and bespoke orchestrators that connect Stripe, HubSpot, Notion, Jira, Sheets and SAP without brittle Zaps.
Models & tooling
We pick the model that fits the job, the budget and the latency target.
Find the AI use-case that pays back fastest.
Free 60-minute discovery: we map your workflows, score the candidates, and propose a 4-week pilot with hard success metrics.
Explore our other services
Frequently asked questions
What kinds of AI projects do you take on?
LLM integrations (Claude, GPT, open-weights), retrieval-augmented generation over your own documents, tool-using agents, internal copilots, and workflow automation. We start with a scoped pilot tied to a measurable outcome, not a science project.
How do you keep AI outputs accurate and safe?
We ground responses in your data with retrieval and citations, add evaluation suites and guardrails, keep humans in the loop where the stakes are high, and track quality and cost in production — typical evaluated hallucination rates stay under 5%.
How fast can we get an AI feature into production?
A focused pilot usually reaches production in about four weeks. We instrument it from day one so you can measure real ROI before scaling it across the business.
Do we need our own ML team or infrastructure?
No. We build on managed models and your existing cloud, and hand over documentation, evaluations and runbooks so your team can own it — or we operate it for you.