Custom AI software that survives a Monday.
Agents, voice AI, document intelligence, and copilots, built in your environment, on your data, with evals and security review in the first release. Not a demo. Production.
What we build
Systems that do work, not just answer questions.
Every offering ships as production software in your environment, not a prototype that dies after the demo.
AI agents and agentic workflows
Multi-step agents that take actions, not just generate text. Built with defined inputs, checks, and failure states so behavior stays predictable in production.
Voice AI
Voice agents for inbound and outbound calls, IVR replacement, and internal ops. Latency, interruption handling, and escalation paths built in from the first release.
Document intelligence
Extraction, classification, and review over contracts, claims, filings, and unstructured records, with human-in-the-loop checkpoints where the cost of a miss is high.
Internal copilots
Purpose-built assistants for support, sales, ops, and engineering teams, scoped to your data and your workflows instead of a generic chat window.
Data pipelines and RAG
Retrieval systems and data pipelines that keep answers grounded in your actual records, with source citations and freshness your team can audit.
Integrations with existing systems
Connections into the CRM and ERP you already run: Salesforce, HubSpot, NetSuite, and the rest of the stack, so AI output lands where your team already works.
How we work
A fixed scope, then a working cadence.
Four phases. You see the architecture before we build, and working software before the engagement ends.
- 01Week 1
Scope
We map the workflow, the data sources, and the failure modes. You get a written scope and a fixed price before any code is written.
- 02Weeks 2 to 3
Architect
System design, model selection, and integration plan. You see the architecture and sign off before the build starts.
- 03Weeks 4 to 9
Build
We ship in your environment on a working cadence, with evals and logging in the first release, not the backlog.
- 04Weeks 10 to 12
Harden and hand off
Security review, runbooks, and training for your team. You get the repo, the documentation, and the people who can run it.
Production hardening
Shipped means it can take a hit.
Every build includes the three things demos skip and production requires.
Evals
Automated test suites that score output quality before and after every change, so regressions get caught in CI, not by a customer.
Observability
Logging, tracing, and cost dashboards on every agent and pipeline, so your team can see what happened and why, not just that something broke.
Security red-teaming
Adversarial testing for prompt injection, data leakage, and jailbreak attempts before launch, with the fixes shipped, not just documented.
Tech stance
Model-agnostic. Cloud-agnostic. Yours either way.
We pick the model that fits the job and the budget: Claude, OpenAI, Gemini, or an open model, and we design so swapping it later costs a config change, not a rewrite. Deployment runs on Google Cloud, AWS, or Cloudflare, matched to where your team already operates.
No hostage code and no black boxes. What we build lands in your repositories, on your infrastructure, under your controls.
What you get at handover
- Production codebase in your repositories
- Architecture and decision documentation
- Eval suite and observability dashboards
- Security review and red-team findings
- Runbooks and operator training
- A 90-day support window after handover
Questions
What buyers ask before a build.
If yours is not here, ask it on the scoping call.
We are model-agnostic: Claude, OpenAI, Gemini, and open models, chosen for the job and the budget rather than a house preference. We deploy to Google Cloud, AWS, or Cloudflare depending on where your existing stack already lives.
Most projects run 6 to 12 weeks from a signed scope to a production handover. Voice AI and agent systems with real integration work land toward the longer end.
Every build includes a 90-day support window after handover. Past that, you can run it with your own team using the documentation we leave behind, or move to a retainer if you want us to keep watching it.
Your data stays in your environment under your controls. We do not train models on your data, and retrieval and pipeline work is scoped to systems you already own or approve.
Yes. Most builds run inside your repositories with your engineers in the loop from day one, since they are the ones operating the system after we leave.
Next step
Bring us the workflow. We will scope the build.
One call to map the workflow, the data, and the cost. You leave with a written scope, not a sales pitch.