Production AI for legal, finance & data teams

Most AI gets stuck in a demo. Ours ships to production.

You've sat through pilots that stall and tools that never quite fit. We build AI agents, document intelligence, and analytics that actually ship, running on the data and workflows your team already uses and deployed inside your own environment. Built for legal, finance, and data teams.

See the proof

30 minutes, one workflow, a clear plan to production. No commitment.

Already live in production
Your data never leaves your walls
NDA-bound & audit-ready

Your documents, data, and workflows could be doing so much more. We put AI to work on them and ship it live inside your business, real results in production, never just another demo.

AI agents that do the work

Give your team and customers instant, accurate answers around the clock. Our AI agents speak plain language, pull straight from your own data, back every answer with real sources, and take action for you, right inside your app, product, or WhatsApp.

Gartner projects ~40% of enterprise apps will embed task-specific AI agents by 2026, up from under 5%. Gartner, 2025

Turn documents into decisions

Stop paying people to dig through paperwork. We turn your contracts, invoices, filings, and case files into instant answers, risk flags, and decisions, the same engine behind VerifiableContract, our own contract platform.

Knowledge workers spend roughly a fifth of the workweek just searching for and gathering information. McKinsey Global Institute

Automate the busywork, or build it new

Wipe out repetitive work by connecting the tools you already use, and when you need the whole product, web, mobile, real-time backends, even AI voice, we build and ship the entire system, not just a model.

01Proof, not promises

We've already done it for teams like yours.

Every project below started with a team stuck on a hard, expensive problem and ended with AI running in production. One is our own product; the rest are client work under NDA. Here's the problem, what we built, and what changed.

VerifiableContract

Agent-based contract lifecycle management

Case 01

Our own product · verifiablecontract.com

Problem

Your contracts live scattered across PDFs, Word files, spreadsheets, scans, and email threads, and your legal and procurement teams burn their days reading, comparing, and drafting by hand. Every manual review is a compliance risk, and every missed renewal is money walking out the door.

Poor contract management costs organizations around 9% of annual revenue. World Commerce & Contracting

System built

We built an agent-based contract platform that pulls every legacy and new contract into one AI repository, extracts the metadata, and maps clauses across any format, then reviews each contract against your playbook for compliance and risk. Your team just asks questions in plain language and gets answers. Drafting templates, obligation tracking, automatic renewal alerts, and one-scan QR retrieval come built in, and anyone can verify a contract's authenticity in minutes.

Any format in · one governed repository out

Result

50–70%

less time and manual effort per contract

Minutes

to verify a contract's authenticity

One

repository for legacy + native contracts

Deployment

Plugs into DocuSign, Adobe Sign, Zapier, Google Drive, and Amazon S3, with contract storage on your own premises whenever you need it.

Document verification platform

Document AI, computer vision & compliance

Case 02

Regulated enterprise · name withheld (NDA)

Problem

Every submission-ready file ran 100–200 pages, and a person had to check all of it by hand: index accuracy, page continuity, duplicates, blanks, and up to 200 signatures and stamps, before signing off. It was slow, draining work, and a single miss carried real compliance consequences.

System built

We built a document-AI pipeline that ingests an entire file and checks it end to end the way a careful reviewer would. OCR and handwriting recognition read printed, handwritten, and alphanumeric page numbers, computer vision catches signatures and stamps on every page, and everything is cross-checked against the file's index. Every discrepancy lands in one automated report, and a human reviewer still gives the final sign-off.

VerifiedDuplicateBlank pageSignature missingIndex mismatch

Signature detected

Stamp detected

OCR + handwriting

One automated pass · 5 discrepancies raised for human sign-off

Result

Whole-file verification (index, page continuity, duplicates, blanks, signatures, and stamps) in one automated pass
OCR and handwriting recognition across printed, handwritten, and alphanumeric page numbering
Computer-vision signature and stamp detection on every page, with discrepancies flagged for review
Human-in-the-loop sign-off, so a person always makes the final call

Deployment

Access-controlled review workflow with role-based reviewer approval, built for regulated, compliance-driven environments.

Real-time conversational AI platform

Agentic AI, RAG & distributed systems

Case 03

High-traffic consumer platform · name withheld (NDA)

Problem

A huge consumer audience wanted instant, trustworthy answers to open-ended questions, with live information, cited sources, and multiple languages, at a speed and scale that static pages and off-the-shelf chatbots couldn't touch.

System built

We built a production conversational-AI backend that streams answers in real time, remembers the conversation, and works across any LLM provider, powered by LangChain and LangGraph agents on an async FastAPI service. Behind it, a distributed engine searches, crawls, and retrieves live sources over the Model Context Protocol, reranks what matters, and cites every source automatically. The same multi-tenant foundation now serves more clients, including on WhatsApp.

One multi-tenant runtime · many clients and channels

Result

Real-time streaming chat with automatic source citations and multilingual, follow-up-aware answers
Agentic tool use (live web search, crawling, and RAG retrieval) orchestrated with LangGraph
Horizontally scaled, containerized services with queue-based distributed processing built for production load
One multi-tenant codebase reused across multiple clients and channels, including WhatsApp

Deployment

Docker Compose deployment with horizontally scaled workers, rate limiting, and hardened security headers, deployable inside your own cloud.

Conversational data-intelligence platform

Text-to-SQL agents & self-serve analytics

Case 04

Innovation & investment ecosystem · name withheld (NDA)

Problem

Investors, partners, and analysts were sitting on a rich company database they couldn't actually use. Every question meant waiting in line for someone to write SQL and build a report, so most questions never got asked and the data went to waste.

System built

We built a conversational agent that turns plain-language questions into governed SQL against the live database, tailors every answer to the user's role, and remembers context across a whole conversation. Now non-technical users explore, sort, count, and group the data themselves, with guided prompts to get started and a saved, searchable history.

Plain language in · governed SQL out

Result

Natural-language questions translated into governed SQL over your live database
Role-aware answers tailored to each type of user
Multi-turn conversation memory with saved, searchable history
Self-serve discovery and matching, with no analyst or hand-written SQL required

Deployment

Web-based chat layered over your existing structured data, with role-based access control.

02Capabilities

Whatever your problem, we've built for it.

You shouldn't need five vendors to get one system live. Every capability below is proven in production, and it's the same range we'd point straight at your problem, whatever shape it takes.

AI agents & LLM engineering

Autonomous agents
LangChain · LangGraph · LlamaIndex · CrewAI
Multi-LLM: OpenAI · Claude · Gemini · Groq
Human-in-the-loop

Grounded retrieval

Retrieval-augmented generation
Semantic search & embeddings
Vector databases & knowledge graphs
Rerankers

Document & data intelligence

Intelligent document processing
OCR & handwriting recognition
Computer vision (signatures & stamps)
Risk scoring, compliance & audit

Data, analytics & ML

Analytics & business intelligence
ML inference & prediction
Identity resolution & unification
Privacy-first data pipelines

Automation & integration

n8n (self-hosted & cloud)
Workflow & process automation
API & webhook integration
DocuSign · Adobe Sign · Zapier

Product engineering & delivery

Next.js · React · TypeScript
Native iOS / mobile
FastAPI · PostgreSQL · Redis
Distributed systems · Docker

03How it works

Messy inputs in. Answers you can trust out.

No black box. Your documents and data go in, and clear, cited, risk-scored answers come out, with AI agents doing the heavy lifting, a human in the loop, and an audit trail on every step. All of it inside your own environment.

Runs inside your cloud, hybrid, or on-prem environment

Ingest

PDF
Word
Excel
Scans
Databases

Parse

OCR
Handwriting
Computer vision
Embeddings

Reason

RAG
LangGraph agents
Text-to-SQL
Clause & risk checks

Review

Human-in-the-loop
Escalation

Deliver

Chat Q&A
Alerts
WhatsApp
Audit logs

FoundationVector DB · Open-source & enterprise LLMs · LangGraph agents · n8n workflow automation · Your policies & SOPs

04Security & ownership

Your data. Your models. Your rules.

Before you trust anyone with your data, you need three answers: where it runs, who owns it, and how every decision is tracked. Here are ours, up front and in writing.

It runs where you want it

Private cloud, hybrid, or fully on-prem, with open-source LLMs or enterprise APIs like OpenAI and Claude. Your data never has to leave your perimeter.

You own all of it

The models, the pipelines, and the data all stay yours. No lock-in to our infrastructure, and no bet on a single model vendor.

It plays by your rules

Every workflow enforces your own SOPs, legal policies, and regulatory requirements, never generic defaults.

Built to survive an audit

Every extraction, query, and agent decision is logged and exportable, ready for review before and after any audit.

Deployment models

Private cloud

open-source LLMs

Enterprise APIs

OpenAI · Claude

Hybrid

split by sensitivity

On-prem

fully inside your walls

05FAQ

Everything you're probably wondering.

No jargon and no dodging, just straight answers on how this works, what you own, and how we get started together.

What does MindzKonnected do?

MindzKonnected builds and ships production AI systems (autonomous agents, grounded retrieval, document and data intelligence, analytics, and workflow automation) and deploys each one inside the client's own cloud, hybrid, or on-prem environment rather than leaving it as a prototype.

Can the AI systems run fully on-premises?

Yes. Systems can run in a private cloud with open-source LLMs, on enterprise APIs such as OpenAI and Claude, in a hybrid setup, or fully on-prem. Data never has to leave your perimeter.

Do we own the models, pipelines, and data?

Yes. Models, pipelines, and data stay yours. There is no lock-in to MindzKonnected infrastructure or to a single model vendor.

Is our data used to train third-party models?

No. Deployments run inside your environment under your own policies, and your documents and data are not used to train external models.

Which LLMs and frameworks do you work with?

Multiple providers, including OpenAI, Claude, Gemini, and Groq, plus open-source models, orchestrated with LangChain and LangGraph and integrated over the Model Context Protocol. The provider is matched to each use case rather than fixed up front.

What kinds of problems do you take on?

Document- and data-heavy workflows: contract management, document verification and compliance, conversational agents with cited answers, and plain-language (text-to-SQL) analytics over structured data, among others.

How does an engagement start?

With a technical scoping call around one workflow: a contract type, a document set, a support queue, or a process to automate. We map the system, the deployment model, and the measurable outcome before you commit to anything.

How do you handle confidentiality and audits?

Engagements are NDA-bound. Every extraction, query, and agent decision is logged and exportable for pre- and post-audit review, and every workflow enforces your SOPs and regulatory rules with a human in the loop.

What measurable results have you delivered?

VerifiableContract, our own contract management platform, reduces the time and manual effort per contract by 50–70%. Client outcomes are shared directly during scoping, since that work is covered under NDA.

How is pricing structured?

Pricing is scoped to the specific workflow and deployment model, and is discussed after the initial scoping call once the system and outcome are mapped.

Still have a question?

Bring it to a free 30-minute strategy call and we'll answer it directly, no commitment.

Let's build it

Your first AI win is one call away.

Bring one workflow: a contract type, a document set, a support queue, anything you want off your team's plate. In 30 minutes we'll map the system, the deployment, and the outcome, no commitment and no jargon, just a clear plan to get AI working for you.

sandeep@mindzkonnected.com +91-8595797775