AI & Agent Development Services
Answers from the source, or a refusal
The reply comes from your documents. It quotes the passage that supports it. If that passage is not in the file, the reply says so.
Examples
The engineering problem, in a few shapes. Retrieval, a citation, and a point where the model has to stop.
- 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.
- 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. The model writes the reply from the retrieved passage.
- 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.
- 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 technical situation
The reply sounds sure, and the passage does not support it. Or the passage is not in the file at all.
How we'd approach it
We index the real documents, retrieve the passage that answers the question, and attach it to the reply. The model says when it does not know. A person stays on any decision that is expensive to get wrong.
What you'd get
You leave with those answers running on your files, the code.
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. It ships in 1 to 3 weeks against your real documents, not a test corpus.
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.
Good retrieval so the model has the right context. Verification steps so it can say 'I don't know' when it has no basis. Hallucinations do not disappear entirely, but the rate drops to an acceptable level.
What you get
- A working fix on your data
- You keep the code
- 2 to 5 weeks when the owner and the files are available
Let's talk about your case
Talk to the Lab
Tell us the problem in a few lines. What the team does today, and where it breaks. We reply the same business day.
What happens next
- We reply the same business day
- Diagnostic in 3-5 days
- Working fix in 1-3 weeks. Technical plan in the last days, with the work done.
Start here