AI you can measure, not just demonstrate
A prototype that impresses in a meeting is not yet a system your business can depend on. We build ai tools, retrieval, autonomous workflows and custom LLM applications, with the evaluation set, accuracy target and cost ceiling agreed before development starts.
Measured before it ships
- Our belief
We agree how AI will be judged before we build it
Discovery produces the evaluation set, the accuracy target, the cost ceiling and the escalation path, all signed off with you. From the first sprint, quality is a number you can see rather than an opinion in a review.
- 01
AI tools and assistants
Grounded in your data, with guardrails and a defined handoff to a person, so they complete real tasks instead of improvising answers.
- 02
Retrieval and knowledge systems
Answers drawn from your source of truth, with citations your team can check and a record of what was retrieved.
- 03
Autonomous workflows
Agents that act, log every action, and escalate to a person the moment confidence drops below the threshold you set.
- 04
Custom LLM applications
The right model for the task, with the evaluation coverage and cost controls that make it safe to put in front of customers.
- 05
AI inside your existing product
Intelligence added behind clean interfaces, with a rollback path at every step and no rewrite of what already works.
The four ways an AI project usually fails
Each one is closed before development starts, in the discovery document you sign off.
Prototype risk
A demo that impresses in a meeting is not a system. The evaluation set, the accuracy target and the failure cases are agreed in discovery, before a model goes anywhere near production.
Runaway cost risk
Token spend and latency are tracked from the first sprint against the cost ceiling you signed off, so unit economics are a number on the demo, not a surprise at scale.
Vendor lock-in risk
Retrieval, orchestration and evaluation sit behind our own interfaces on open standards, so changing model provider is a configuration change rather than a rebuild.
Handoff risk
You own the prompts, the evaluation set, the pipelines and the repositories at every milestone, and the closure report records how each was tuned and why.
Mature tools, open interfaces, no black boxes
The stack is fixed in discovery against your architecture, then it does not drift mid build.
- Models & frameworks
- PythonPyTorchOpenAI APILangChainLlamaIndex
- Retrieval & data
- PGVectorPineconePostgreSQLRedis
- Serving & cloud
- FastAPIDockerAWSGitHub Actions
Ready to build with bluesheep?
- Ship AI tools
- Take a prototype to production
- Stand up cloud infrastructure
- Build a payments platform
- Extend my engineering team
- Audit my architecture
- Rebuild a legacy system