We build RAG and semantic search systems for companies whose documents are too complex for off-the-shelf AI — contracts, proposals, RFIs, XER schedules, invoices. Any industry. Any stack.
Most teams attach PDFs to an LLM and get confident wrong answers. The real problem is chunking, retrieval, and domain-aware parsing — not the model.
Splitting by token count mixes topics into one chunk. The embedding represents nothing. Vector search finds nothing useful.
Without re-ranking and section classification, retrieval returns similar-sounding but wrong content — especially on long documents.
Most teams run every query through an LLM. 90% of queries — dates, clauses, numbers — can be answered with semantic search alone at a fraction of the cost.
A generic RAG system doesn't know a liquidated damages clause from a scope section, or an XER activity from a milestone. Domain knowledge has to be built in.
Ask questions in plain language, get answers with exact source citations. Works across contracts, proposals, RFIs, manuals — any document type. Section-aware chunking means right answers 9 out of 10 times.
RAG · Semantic Search · CitationFlag risky clauses, compare versions, extract key terms automatically. Built for legal agreements, SOWs, MSAs, and supplier proposals where every word matters.
Clause Extraction · Risk Flagging · ComparisonParse Primavera P6 XER files, compare versions, detect slippage and negative float. The only document intelligence team that handles construction schedule formats natively.
XER · P6 · Schedule AnalysisAlready building a document-heavy product? We fix the retrieval layer — better chunking, domain-aware re-ranking, lower token costs. Works with your existing stack and team.
Consulting · Implementation · OptimizationBuilt a system that parses Primavera P6 XER files, compares versions, detects activity slippage, and generates executive reports in one click. Bilingual English/Arabic support for Middle East projects.
Travel proposals are notoriously hard to parse — every supplier formats differently, pricing is conditional, itineraries mix structured and unstructured data. Built domain-aware chunking with section classification and re-ranking.
Section-aware chunker that reads document structure, groups content by section, and labels each chunk by what it actually contains. Works for contracts, invoices, legal agreements, project schedules.
We understand your document problem, current stack, and what accurate retrieval would unlock for your team. No pitch deck. Just a real conversation.
We audit your current document AI — chunking strategy, retrieval accuracy, token costs — and deliver a clear implementation plan with expected improvements.
We build the document intelligence layer on your infrastructure, integrate with your stack, and hand over everything — code, models, documentation. You own it completely.
Tell us your document problem. We'll tell you honestly if we can fix it and how long it takes.
No pitch deck · No commitment · Just a real conversation