AI that makes it past the demo.
We build private LLM systems, agents and automation on infrastructure you control, with the access controls, cost limits and monitoring that production needs. We come from operations, so we start with the questions most AI vendors skip.
- 01Employee asks a question
SSO login, permissions checked - 02Retrieval from your documents
only the ones that person may see - 03Model of your choice
open-weight on your servers, or a managed API - 04Answer with sources
every claim links to the document it came from
Most AI pilots never reach the people they were built for
The model is rarely the problem. These are, and they're infrastructure and operations problems, which is exactly what we do.
Legal blocks it, and the pilot dies in a meeting.
Token costs nobody estimated, multiplied by real usage.
No one measures how often it's wrong, so no one trusts it.
The data scientist moves on. Nobody runs it at 3 a.m.
What we build
Private LLMs & RAG
Ask questions of your contracts, manuals, tickets and wiki, with answers that cite their sources and respect who's allowed to see what.
- Document ingestion and vector search (pgvector)
- Permission-aware retrieval tied to your SSO
- Evaluation set to measure answer quality
Good fit if: people spend hours searching internal documents.
Agents & automation
Agents that triage tickets, fill in forms, draft replies and pull context from your systems. A person approves anything that matters.
- Integrations with your ticketing, CRM and email
- Human-in-the-loop approval for every action
- Full audit log of what the agent did and why
Good fit if: a team repeats the same structured task every day.
GPU & MLOps infrastructure
Model serving and GPU scheduling on Kubernetes, with autoscaling and cost controls. We've run GPU workloads for NLP in production.
- Model serving with vLLM on your cluster
- GPU node pools, scheduling and scale-to-zero
- Latency, throughput and cost dashboards
Good fit if: data can't leave your network, or API costs got too high.
AI readiness audit
START HERETwo weeks, fixed fee. You find out what's worth building before you spend on it.
- Use cases ranked by value and effort
- What your data and contracts allow
- Monthly running-cost estimate per option
- A list of what you shouldn't build
Good fit if: leadership wants AI and nobody knows where to start.
Three ways to run AI. We'll help you pick.
| Open-weight, on your servers | Managed, in your cloud | Direct API | |
|---|---|---|---|
| Examples | Llama, Mistral, Qwen via vLLM | Azure OpenAI, Vertex AI, Bedrock | OpenAI, Anthropic |
| Data leaves your network | Never | Stays in your cloud account and region | Yes, under enterprise terms |
| Cost profile | Fixed GPU cost, cheap at volume | Pay per use, cloud billing | Pay per use, fastest to start |
| Best for | Sensitive data, high volume | Regulated teams already on a cloud | Pilots, low volume, best quality |
From idea to production in six weeks
One use case, one success metric agreed on day one, fixed price. If the metric is out of reach, you'll hear it from us in week three.
One use case, one number that defines success, data access sorted.
Ingestion pipeline and an evaluation set built from your real questions.
Working system, scored against the metric every day. Go or no-go at week three.
Deployed on your infrastructure with monitoring, cost limits and runbooks.
What clients ask before an AI project
Which model should we use?
The cheapest one that passes your evaluation set. We test two or three on your real questions and show you the scores and the monthly cost side by side.
Can it run fully on-premise?
Yes. Open-weight models run on your own GPUs, on Kubernetes or bare metal, with no data leaving your network.
How do you deal with wrong answers?
We measure them. Every system ships with an evaluation set, answers cite their sources, and anything that triggers an action needs human approval.
What will it cost to run each month?
You get a running-cost estimate before anything is built, and hard spending limits once it's live. No surprise invoices.
Do you work with Azure OpenAI, Bedrock and Vertex AI?
Yes, along with the OpenAI and Anthropic APIs directly. If you're already on a cloud, using its AI service is often the easiest path through security review.
What if AI isn't the right answer?
We'll tell you. Sometimes a script or a better search index does the job for a fraction of the cost.
Have an AI idea? Let's check it's worth building.
20 minutes with an engineer. You'll leave knowing whether it's worth building, and roughly what it would cost to run.