AI & Agent Development Services
AI that works on your documents and data, not generic chat
We build assistants and agents that answer questions based on your company's documents, classify incoming requests, and support decisions with data. A system that reads what you have and gives a grounded answer instead of guessing.
What we build
We build AI that works on what your company actually has: your documents, your records, and the questions your users send in every day. Every answer comes back with the source passage attached.
- RAG assistants. They answer from your internal documents and cite the source passage on every response, so anyone reading can verify the answer came from somewhere real.
- Autonomous agents for research, classification, and decision support. With human checkpoints wherever the cost of a bad decision is high enough to warrant one.
- Classical machine learning. Classification, regression, clustering, forecasting, and anomaly detection, used wherever a large model would be slower, more expensive, or less accurate than a smaller one.
- LLM pipelines with tools. They call APIs, fetch fresh data, and produce structured output that your systems consume directly, without a human reformatting the response.
- Multi-step workflows. An agent plans, acts, verifies its own work, and hands a clean result to the next step, instead of dumping everything back to a person to sort out.
Technologies
Model choice matters less than the pipeline around it. Most of our work is in retrieval, evaluation, and structured output. Those are the parts that decide whether an answer is correct rather than merely fluent.
- LLMs. OpenAI, Claude, Gemini, and open-source options like Llama, Mistral, Gemma, and Qwen, chosen per task based on accuracy, cost, latency, and privacy needs.
- Vector databases. Pinecone and Weaviate for dedicated workloads, pgvector when you want to keep retrieval inside your existing Postgres.
- Orchestration. LangChain and LlamaIndex for common patterns, custom code when the framework gets in the way of what you actually need to do.
- Classical ML tooling. Scikit-learn and XGBoost for tabular problems where a structured model beats a language model on both quality and cost.
- Embeddings and retrieval. Hybrid search that combines semantic matching with keyword search, because pure vector search misses things a human reader would not.
How we'd work on this
A common situation
The support team answers the same questions every week. Someone in finance spends an hour finding the right clause in a contract. The knowledge base exists but nobody searches it because the search doesn't work.
How we'd approach it
Index your actual documents, build a retrieval pipeline that finds the right passages, wire it to an LLM that answers with citations. Add verification steps so the model can say when it doesn't know, instead of making something up.
What you'd get
A working assistant that answers from your documents with sources cited. A technical plan for the next phase. Baseline metrics on accuracy and response time after running against real queries.
Questions about AI and agents
RAG stands for Retrieval-Augmented Generation. Instead of asking an LLM to answer from scratch, you first retrieve the relevant passages from your documents and pass them to the model as source material. The result is an answer that cites where it came from, instead of sounding confident and being wrong.
We index your documents in a vector database (pgvector, Pinecone, or Weaviate depending on the case), build a retrieval pipeline that combines semantic and keyword search, and wire it to an LLM that answers with citations. The pilot ships in 2-4 weeks against your real documents, not a test corpus.
LLMs when the input is unstructured text, open-ended language, or the task requires reasoning. Classical ML (XGBoost, scikit-learn) when the data is tabular and the pattern is predictable: churn prediction, sales forecasting, fraud detection. Classical ML tends to be cheaper, faster, and more accurate for those jobs.
pgvector when you already run Postgres and do not want another service to operate. Pinecone when load is high and you want a managed service that just works. Weaviate when you need features beyond vector search, like GraphQL or complex filters. For most cases, pgvector is enough.
Three things together. Good retrieval so the model has the right context. Verification steps so it can say 'I don't know' when it has no basis. Evaluation with real questions before going to production. Hallucinations do not disappear entirely, but the rate drops to an acceptable level.
That depends on the choice. When the case allows, we use the OpenAI or Anthropic API under agreements that prevent training on your data. When privacy requires it, we run open-source models like Llama, Mistral, Gemma, or Qwen on your infrastructure or ours, with no data sent to external APIs. The technical plan locks that decision before we build the prototype.
Our differentiators
- Working prototype before any long-term decision
- No lock-in: you keep all code and documentation
- Projects start in days, not weeks
Let's talk about your case
Talk to the Lab
Describe the challenge in a few lines. We'll get back to you to discuss next steps.
What happens next
- 30-min call, no commitment
- Diagnostic in 1-2 weeks
- Working prototype in 2-4 weeks, technical plan in 1 week
Start here