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Most AI initiatives die between the demo and the deployment

The demo is the easy half. What kills a project is the part nobody scopes — the system of record, the guardrails, and who gets paged when it stops.

What usually happens

  • A slick proof-of-concept that never touches a real system of record

  • Consultants deliver a strategy deck, then leave before anything ships

  • No owner for uptime — the agent silently stops and nobody notices for weeks

  • Data leaves the building; legal and compliance block go-live

  • Per-seat AI licences that scale cost faster than value

What we do instead

  • Every engagement ends with a container running in production, not a PDF

  • We operate what we build — health-monitored, logged, on-call

  • Deployed on your cloud or ours; your data never trains a public model

  • Compliance-first architecture — retention, audit trail and access control from day one

  • Flat platform fee, unlimited internal users

Ready to see it in action?

Or call +91 95525 86428

Not every job needs an agent. Three kinds do.

A rules engine is cheaper, faster and never makes anything up. We reach for an agent only when a job passes one of these tests — otherwise we will tell you to write a cron job.

  1. The decision needs judgement

    Every case differs: a re-evaluation request, a refund on a disputed invoice.

  2. The rulebook no longer fits on one page

    Eligibility across forty programmes; fee waivers with six exceptions each.

  3. The input was written by a human

    Handwritten scripts, scanned mark-sheets, a Hinglish WhatsApp at 11pm.

What comes in

“A ticket, a scanned mark-sheet, a WhatsApp message at 11pm”

One agent

  • Instructions

    Your SOP, rewritten as steps it can follow, edge cases included

  • Model

    Decides the next step. Never the final word on money

  • Tools

    Your CRM, ERP and database — every one rated before it is wired in

Every tool is rated by what it can break

  1. ReadsRuns alone
  2. Reversible writesRuns alone, logged
  3. Money, contracts, customer promisesDrafts — your person approves
  • Done, and written to the log

  • Handed to a person, with the trail attached

It hands over when it has tried twice and failed, or when the next action cannot be undone.

We run our own AI agent fleet in production — every day

  • We run it ourselves first

    Our own agents prospect our sales, purge our exam data and watch our servers.

  • Your context, not the internet’s

    Grounded in your data, policies and tone, not generic answers.

  • Auditable by design

    Every action logged, every decision traceable.

  • Draft-and-approve by default

    Money, contracts and promises are drafted by the agent, approved by your person.

What you are actually hiring

22 roles across 9 departments. Each owns one job end to end, and each stops where your person decides.

HR

2 roles

Marketing

2 roles

Sales & Pre-Sales

3 roles

Customer Experience

3 roles

Finance

2 roles

Engineering & IT

3 roles

Governance & Office

3 roles
  • Compliance Officer

    Data retention policy, enforced.

  • Knowledge Manager

    Company knowledge, one question away.

  • Executive Assistant

    Every meeting ends with minutes sent.

Education

3 roles

Government

1 role

Already know which team is drowning? The packs below staff a whole department at once.

Eight agent packs. Start with one.

Packs are modular. Start with one, prove the number, expand. Nothing here requires a platform migration.

Sales

Revenue Pack

Your reps stop doing research, data entry and document assembly — and get back roughly a day a week to actually sell.

  • Prospect & enrich
  • Lead scoring
  • Cold outreach
  • Proposal & RFP
  • Quotation & SOW
  • Deal rescue
  • Meeting debrief
  • Tender war room

Marketing

Growth Pack

The output of a full content and SEO team at a fraction of the headcount — and visibility in AI search before your competitors realise it matters.

  • AI search visibility
  • SEO & ranking
  • Conversion (CRO)
  • Content engine
  • Competitor watch
  • Executive digest

Compliance

Retention Guardian

India’s DPDP Act makes “we kept it forever” a liability. This turns your retention policy from a document nobody follows into infrastructure that enforces itself — and cuts storage cost while doing it.

  • Policy-driven deletion
  • Tiered cold archive
  • Audit trail
  • Database log purge

IT & DevOps

Most monitoring tells you a server is up. Ours tells you whether the work actually got done — and fixes the database before users feel it.

  • Fleet activity monitor
  • Database optimizer
  • Index maintenance
  • Database firewall
  • Uptime & incident
  • Read-only query agent

Knowledge

Company Brain

A private retrieval engine over your documents, tickets, contracts and wikis. When someone resigns, their knowledge stays — and every answer cites its source document.

  • Private document retrieval
  • Cited answers
  • Self-hosted vector store
  • Full query audit log

Finance

Finance Pack

Shortens the cash cycle and stops silent leakage. The vendor spend audit alone usually pays for the pack in month one.

  • Receivables & collections
  • Pricing & discount control
  • Vendor & SaaS spend audit
  • GST & filing calendar

Engineering

Engineering Pack

Raises code quality without adding reviewers. Every pull request is reviewed before a human spends time on it, and production errors are clustered rather than dumped.

  • Automated code review
  • Exception & log triage
  • Self-healing bug loop
  • Dependency & CVE watch

Education & assessment

We operate assessment, admissions and document-verification platforms ourselves. These agents are built on domain knowledge no horizontal consultancy has.

  • Admission counselling
  • Document verification
  • Proctoring review
  • Question paper generation
  • Evaluation QA
  • Accreditation assistant

Ready to scope an agent?

Pick one department. In one day we put one agent live against one metric you care about.

Or call +91 95525 86428

One agent live in one day. Then we run it.

Day one takes one agent, on your data, against one named metric, through the first four stages. Anything touching money, contracts or customers drafts for your approval. You approve the shortlist before anything is built.

  1. Day 1

    Discover

    One workshop to find where time and money leak, and name the metric.

  2. Day 1

    Prioritise

    Pick the agent with the clearest payback; you approve it.

  3. Day 1

    Build

    Set up on your real data and tested in a sandbox.

  4. Day 1

    Deploy

    Production with monitoring, rollback, a named owner and training.

  5. Ongoing

    Operate

    We run it under SLA and report the metric monthly, or hand it over.

Three ways to engage

Readiness Sprint

Fixed fee · 2 weeks · credited against deployment

  • Process and data discovery
  • Prioritised agent roadmap
  • ROI model per agent
  • One working prototype on your data
  • Executive readout

Pack Deployment

Build fee plus monthly operate fee · priced on application

  • Choose any pack from the portfolio
  • Configured to your systems and policies
  • Deployed on your cloud or ours
  • Monitoring, logging and SLA included
  • Unlimited internal users — no per-seat cost
  • Quarterly tuning and expansion

Agent Partner

Annual retainer · dedicated capacity · priced on application

  • A standing agent engineering team
  • New agents built on demand
  • Full fleet operated under SLA
  • Quarterly business review with the board
  • Source code and IP transferred to you

Priced against a metric, not a timesheet. Every engagement is priced against a named business metric. If the metric doesn’t move, we fix it at our cost.

What buyers ask us first

How is this different from an AI pilot?

Every engagement ends with a container running in your production environment, monitored and owned — not with a strategy deck. We operate what we build, so there is always someone accountable for uptime rather than an agent that silently stops and is noticed weeks later.

How do you know a scheduled job actually did something last night?

Most monitoring reports that a server is up. Our Fleet Activity Monitor reports per-container proof of work — messages sent, files migrated, records purged, errors thrown — which catches the job that runs successfully while doing nothing at all. A backup that backs up an empty folder still exits zero.

We are already doing AI internally. What would you add?

The model is rarely the hard part. Monitoring, retries, audit logs, access control and someone on call when it breaks at 2am are what separate a pilot from production. If those are already in place and running, you may not need us — if they are not, that gap is the work.

Our data cannot leave our premises. Is that a problem?

That is our default rather than an exception. Agents deploy as standard containers on your infrastructure, your data never trains a public model, and the audit log is yours. Company Brain in particular is designed to be self-hosted with your own vector store and access controls.

What happens when an agent gets it wrong?

Every agent is draft-and-approve by default on anything touching money, contracts or customer-facing communication — the agent prepares it and your person approves it. Where an agent does act alone, such as deleting expired data or rebuilding an index, the action is either reversible or logged, and we show you the log.

How long before something is actually running?

One day from first conversation to one agent live, on the single process with the clearest payback. If you want a prioritised roadmap and an ROI model across many processes first, the two-week Agent Readiness Sprint covers that.

Do we get locked in?

No. Agents run as standard containers on your infrastructure. If you end the engagement they keep running, and on the Agent Partner engagement the source code and IP transfer to you.

One agent, or a team of them?

One, first. Most of our own fleet runs as a single agent with a handful of well-named tools. We split into a manager with specialists only when one agent starts picking the wrong tool — every extra agent is one more thing to monitor at 2am.

Free presentation · 13 slides · PDF

Everything on this page, in a deck you can forward

The presentation we walk boards and CXOs through — the packs, the guardrails, the one-day path to a live agent and the three commercial models.

  • 22 AI employee roles across 9 departments
  • Eight department packs, and the agents inside them
  • One day from first conversation to one agent live
  • Three commercial models, and what each includes
Download the deck

No cost. Work email and organisation only.

Which process would you like to automate?

Tell us your department, the systems you use and the work that slows your team down. We will help identify a useful first workflow and arrange a relevant demo.

Discuss your workflow

Or call +91 95525 86428

Free · 17 slides · PDF

Before you go: meet your 22 AI employees

Which jobs AI can take off your team, and where your people stay in charge.

  • 22 AI employees across 9 departments
  • Input → AI → result, for every role
  • Where your people always decide
  • How to go live in one day

Instant download. Three fields.

No cost. Work email and organisation only.

Book a demo

See it running on your process