A context layer connects your documents, email, ERP and CRM so AI answers from your own information, cites its source, and respects every permission.
Every answer links to the document, record or message it came from.
If a person can't open a file, the AI can't use it for them either.
Proposals, replies and reports built from past work and current records.
New documents and records are indexed as they arrive.
Private and local first for critical and sensitive data. Partnered cloud models only when a task needs them, behind a policy gate, with audit logs and monitoring you can see.
What we use for RAG & context layers, and what each piece is for.
A working AI product in your users' hands in seven days, built on our proven components.
No-obligation free MVP for startups. Scope agreed in the free consultation; you keep it either way.
Built your app with AI coding tools but it isn't secure or won't scale? We review it free and fix the critical security and scaling issues free.
PDF, Word, scans via OCR, SharePoint, Google Drive
Email, chat and meeting transcripts
ERP, CRM, helpdesk and databases through connectors or APIs
Milvus, pgvector, Qdrant for semantic search
Solr / OpenSearch for exact terms and IDs
Cross-encoder models that put the best passage first
Pipelines for chunking, retrieval and answering
Plans, searches, digs deeper, organises
Every answer linked to its passage
Access rules copied from source systems
Personal data masked before any model
Who asked what, and what was returned
Typical phases and timelines; your plan is agreed after the free consultation.
Which systems, which documents, which permissions
1 weekOne department's knowledge, evaluated on real questions
2–4 weeksAll approved sources, permissions enforced, monitored
4–8 weeksNew sources and use cases on the same layer
OngoingFrom products we built and run, and engagements we measured.
RAG over 1.2M+ court orders plus the firm's own matters, with a citation check that allows zero invented cases; 6.2× faster case prep across 240+ practices.
AI answers only from the user's own sources, with page-level citations, and sends only the needed passages to the model.
The technical detail, for your engineers.
Connectors for files, email, chat, ERP and CRM; OCR and vision for scans; chunking tuned per document type.
Hybrid search (vector plus keyword), reranking, and multi-step retrieval that plans, searches, digs deeper and organises.
Access control lists copied from source systems and enforced at query time, not just at indexing.
Grounding checks, citation validation and an evaluation set of real questions scored before and after every change.
Answers must be grounded in retrieved passages and cite them; ungrounded answers are refused or flagged.
Yes. Permissions are enforced when the question is asked, not just when documents are indexed.
OCR and vision models turn scans into searchable text.
Yes: index, models and search can all run in your environment.
Files, email, chat, ERP, CRM, helpdesk and databases; anything with an API or export.
New and changed content is indexed continuously, usually within minutes.
Most major languages, including Indian languages; we test quality on your content.
An evaluation set of real questions is scored before launch and after every change.
No. We use private models or enterprise agreements that forbid training on your data.
Book the free two-hour consultation. We look at one real workflow and tell you whether this technology fits.
Yes. Engagements we take on carry our 10× productivity guarantee on the agreed workflow, or the fee comes back.
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assets/img/projects/lexedge.pngOne AI workspace for legal drafting, research and citation checks
240+ practices using it
Thumbnail:
assets/img/projects/plasmatext.pngResearch workspace where every AI answer cites its page
23 report templates
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assets/img/projects/agnidoot.pngOdoo ERP built from your documents, with private AI inside
15 days to go liveTwo free hours on a real workflow. We'll tell you whether it fits, and what it would take.
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