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AI & agents

AI that works on your own data

RAG pipelines, MCP servers and agents, connected to the systems you already run.

We build AI that answers from your documents and your ERP, not from guesswork. It cites its sources, respects who is allowed to see what, asks before it acts, and runs on a hosted model or on your own servers.

Assistant · connected to ERPNext and the Construction AppExample
Which sites didn't send a DPR yesterday?
MCP toolconstruction.get_dpr_status(date="2026-10-20")read-only · your permissions
2 of 12 siteshave no DPR for 20 Oct:Clinic fit-out, Sector 62(last DPR 18 Oct) andOffice floor, Dwarka(last DPR 19 Oct). Want me to remind both site engineers?
DPR-1184DPR-1191Project list
Send remindersNot now

Retrieval-augmented generationRAG pipelines

Answers from your contracts, manuals, policies and records, with the passage they came from.

  • Ingestion from file shares, email, ERPNext and other databases, kept fresh on a schedule
  • Layout-aware parsing and chunking for tables, scanned PDFs and long documents
  • Hybrid search: vector similarity plus keywords, then re-ranking
  • Permission-aware retrieval that mirrors access in the source system
  • Citations on every answer, so people can check it
  • Evaluation sets that measure answer quality before and after every change
SourcesPDFs, Word, Excel, email, ERPNext records
Parse & chunkLayout-aware, with metadata
EmbedHosted or open-weight models
IndexVector + keyword (hybrid)
RetrieveRe-rank, filter by access
AnswerGrounded, with citations
Access filter: each user only retrieves what they're allowed to see in the source system
AI assistantClaude or any MCP client
MCP
Your MCP servertools · resources · promptsauth · audit log · approvals
API
ERPNextConstruction AppDatabases & files

Model Context ProtocolMCP servers

MCP is the open standard that lets AI assistants use your systems as tools. We build MCP servers that give assistants safe, scoped access to ERPNext, Frappe apps and your own software.

  • Tools such as check stock, create a lead or get project status
  • Resources such as reports, documents and records the assistant can read
  • The user's own permissions, through OAuth or API tokens, never a super-user
  • Read-only by default, with an approval step before any write
  • Audit log of every tool call
  • Works with Claude and other MCP-compatible assistants
Agents & automation

Multi-step work, with a person in the loop

Agents that plan, call tools and draft the result, then wait for someone to approve it.

Requirements to backlog

An RFP or a one-line idea turned into epics, user stories, use cases and non-functional requirements.

Documents to drafts

Invoices, BOQs and purchase orders read and turned into draft ERPNext records for review.

Reports that write themselves

A week of DPRs summarised into the client's weekly update, ready to edit and send.

Portals without APIs

Browser agents that fill in government and supplier portals that offer no integration.

Ask your ERP

Plain-language questions about sales, stock and projects, answered from live data.

Triage and routing

Support emails and tickets classified, summarised and sent to the right team.

Deployment & governance

Your choice of model, your rules

Hosted models

Claude, GPT and Gemini through enterprise APIs, when data policy allows.

Open-weight models

Llama, Mistral or Qwen on your own servers or a private cloud in India.

Guardrails

Scoped tools, input and output checks, and approval for actions.

Run and measure

Evaluation, monitoring and cost limits after go-live.

Built in Python and Java (including Spring AI), with LangChain or LlamaIndex where they help, and plain code where they don't.

How a pilot runs
  1. Pick one questionSomething your team asks every day
  2. Prepare the dataSources, access rules, test set
  3. PrototypeA working assistant on real data
  4. EvaluateMeasured against the test set
  5. HardenSecurity, approvals, monitoring
  6. Roll outOne team first, then the rest

Bring one question your team asks every day.

We'll show you what an assistant grounded in your own data would answer, and how it would know.