A chat box is not a strategy. We build retrieval-augmented systems that answer from your private knowledge, cite the source, and respect who is allowed to see what.
Highly paid staff spend hours searching scattered PDFs and fragmented knowledge bases.
A system that retrieves the exact paragraph from internal archives and answers with source citations.
New staff lack institutional knowledge and need constant peer support to do the work.
An always-available assistant that guides employees through specific standard operating procedures.
Pulling clauses or figures from thousands of unstructured contracts is slow manual labor.
Software that scans unstructured text, extracts the entities, and populates a queryable database.
Every answer traces back to the exact source passage .
A query runs against embeddings in a vector store , the most relevant passages are retrieved under access controls, and the model synthesizes an answer with citations pinned to each fact. Nothing is invented, and every claim links back to a document.
Verification architectures drop enterprise error rates from 8.3 percent to 3.2 percent in production.
Combining dense embeddings with keyword matching raises recall, forcing teams past basic vector search.
Enterprise RAG runs $8,100 to $19,500 a month, so embedding and inference costs must be tuned.
A chat box looks productive in a pilot demo. Then hallucinated answers, manual document hunts, and per-output correction costs erode the time savings the rollout promised. That retrieval and guardrail layer is where most GenAI programs stall—and where cited, access-controlled answers earn their place.
Global business losses attributed to AI hallucinations in 2024
Suprmind, 2026
Average cost to correct a single hallucinated output in legal processing
natlawreview.com, 2026
Average correction cost per hallucinated output in financial services
natlawreview.com, 2026
Manual corrections per month at an 8.3 percent error rate on 50,000 documents
natlawreview.com, 2026
The model synthesizes only from the exact documents retrieved, with clickable source citations.
Output guardrails and evaluation frameworks measure hallucination control, not vibes.
The retrieval layer inherits your directory permissions, so users see only what they may.
Small, fast models handle routing; heavy models are reserved for complex synthesis.
For SLED scope under NAICS 511210, we index public records and 311 knowledge bases and answer constituent queries as your subcontractor, never facing the agency.
NDA-first, subcontract-only. We work behind the prime, under your brand. We do not pursue prime contracts and we never face the agency.
Data stays yours. Private API endpoints and zero-retention agreements mean your data never trains a public model.
Deployed in your VPC. Models run inside a secure virtual private cloud with role-based access at the retrieval layer.
No. We use private API endpoints and zero-retention agreements so your data never leaves your controlled environment.
Strict retrieval grounding and multi-model verification mean the model can only synthesize answers from the exact documents provided to it.
The retrieval engine inherits your existing active directory permissions, so users only retrieve documents they are authorized to view.
Query volume and token usage. We optimize by using smaller, faster models for routing and reserving heavy models for complex synthesis.
This is the retrieval and LLM-application layer, the part that grounds answers in your documents. Agents add orchestration and tool use on top of it.
Tell us where your team loses hours hunting through documents. That is where the first index goes.
Scope a RAG build
pgvector 
LangChain