Bespoke agents, on your data, in your control.
When the use case is too specific for a vendor and too important to get wrong — we build from the model upward.
Not every problem has a SaaS answer. Sometimes you need an agent trained on your knowledge base, running on your infrastructure, with guardrails specific to your risk profile. That's the work we love most.
How it runs
The agent sits between your signals and your actions.
arthat / custom-agent-console- 00:12
Opened ticket #8941 — retrieved 4 policy docs - 00:11
Drafted response cited 3 KB articles - 00:11
Refund initiated for order #22318 - 00:10
Escalated to human — low confidence on delivery ETA - 00:09
Closed ticket #8940 after confirmation - 00:08
Ran nightly eval — 94.2% accuracy vs golden set
What we automate
Here’s what usually lives inside an engagement.
- Custom agents fine-tuned on your domain knowledge
- RAG systems (retrieval-augmented generation) over your proprietary data
- Private LLM deployments — on your cloud, no data leaving your infrastructure
- Multi-agent systems with specialist roles and handoffs
- Embedded agents inside your product (SDK + UI components)
- Evaluation pipelines — so you know when the model regresses
Audience
Who this is for
CTOs at data-sensitive companies
You can't send customer data to a third-party model. You need this on your VPC.
Product teams embedding AI
You need an agent inside your product, not a chatbot on the side.
Operators with specific edge cases
Every SaaS AI tool solves your problem 80%. You need the other 20%.
Typical outcomes
What this changes
Typical accuracy on evaluation sets at launch
Data leaves your environment on private deployments
Automated regression tests on every model version
Median time to production-grade v1
Comparison
How we're different
Tool stack
Built on the tools you already use
We build on the tools your team already uses — no rip-and-replace.
How we work
Four phases. Nothing hidden.
We scope exactly what the agent will do, what it won't, and how we'll measure success before any code is written.
Ingestion, chunking, embedding, and retrieval — the boring work that decides whether RAG actually works.
We build the agent loop, prompts, tool calls, and the evaluation harness alongside. No blind deploys.
AWS, GCP, Azure, or on-prem. We bring up monitoring, logging, and runbooks.
Proof
Work we've shipped
FAQ
Questions we get a lot
Ready to automate the work that’s slowing you down?
Book a 30-minute discovery call. We’ll listen, scope, and tell you honestly whether AI is the right tool for the job.