LLM applications that answer from your data, and get checked
We build assistants and pipelines on large language models. They answer from your documents and systems. We measure them against an evaluation set and serve them where your data is allowed to live.
- A look at your setup and problem
- A straight answer on whether AI fits
- No hard sell
What we build
RAG: answers from your documents
A question-answering assistant over manuals, procedures, tickets or reports. Every answer cites the passage it came from, so people can check it instead of trusting it blindly. Retrieval is built around your documents: chunking, search and ranking tuned to your content, because most wrong answers start with the wrong context.
Extraction: documents into structured data
Turn PDFs, forms, emails and scans into validated fields your systems can use. Outputs are checked against schemas and business rules, and low-confidence cases are routed to a person rather than guessed.
Agents: tool-using workflows
Agents that call your APIs and databases to complete a defined task. Permissions are scoped to the tools they are given, every step is logged, and a human approves the actions that matter. The scope stays narrow on purpose: a small agent that works beats a general one that sometimes does.
Self-hosted: open-weight models on your GPUs
Your data leaves your infrastructure only if you choose a hosted model. When it cannot leave, we serve open-weight models on your own hardware or private cloud, sized and optimized for your latency and cost budget.
Evaluation set first, not after launch
A chat demo is easy. A system people trust every day needs measurement from the first week. We agree with your team on real questions and correct answers before building, so every change is measured against them. The same set keeps checking quality after launch.
Guardrails and permissions
Users only see what they are allowed to see, so retrieval respects the access rules you already have. Agents only touch the tools they are given. When the system is unsure, the answer says so.
Serving, latency and cost
Hosted API or self-hosted model, chosen on your constraints: data sensitivity, response time and budget. We size and optimize the serving path like any other GPU workload, using the same inference engineering we apply to real-time video: batching, quantization and profiling the full request path.
MLOps: monitoring and feedback in production
Language-model systems degrade quietly too. Documents change, questions shift and a provider updates a model. We add logged traces, automated quality checks and feedback loops, so a drop in answer quality shows up on a dashboard before it shows up in complaints. This is the same MLOps discipline we apply to computer-vision systems.
Pairing with camera systems, and when not to use an LLM
Vision events and reports can feed an assistant, so people can ask about what the cameras saw in plain language. We also tell you when an LLM is the wrong tool. Sometimes a search box, a rule or a simple classifier does the job, and the first call is for that answer.
- RAG with citations
- document extraction
- tool-using agents
- evaluation sets
- guardrails & permissions
- self-hosted open-weight models
- GPU serving & latency
- cost optimization
- quality monitoring
- vision + LLM pairing