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AI & Data

AI that does the work,not just the demo.

Automation deployed into your actual systems, with the cost per run measured and the human kept in the loop where it matters.

Sound familiar?

  • Three people spend most of their day copying data between systems that don't talk to each other.
  • Invoices, forms and emails arrive as PDFs and someone types them in by hand.
  • You piloted an AI tool last year, it demoed beautifully, and it never reached production.

If any of those land, this is the page for you. Here's how we approach it.

What you get

What ai automation actually changes.

01

Hours back, every week

The repetitive work that consumes your team's day handled automatically, with exceptions escalated rather than silently guessed at.

02

Fewer errors than manual entry

Extraction and validation applied consistently, with confidence thresholds that route uncertain cases to a person.

03

Runs against your real systems

Connected to your ERP, CRM, email and databases — not a standalone tool someone has to remember to open.

04

Costs you can predict

Token and API usage measured per workflow, so you know the cost per invoice before you scale it.

05

Humans stay in control

Approval steps where the decision matters, with a full record of what the system did and why.

06

Starts small, proves value

One workflow first, measured against the manual baseline, before you commit to a programme.

What's included

Everything in the engagement.

Document processing

Invoices, POs, forms and contracts extracted and validated.

Email triage

Classify, route, summarise and draft replies for review.

Data entry automation

Move data between systems without a human retyping it.

Approval workflows

Route by rules, escalate on exception, log every decision.

Report generation

Recurring reports assembled and distributed on schedule.

System integration

ERP, CRM, accounting, WhatsApp, email and databases.

Human-in-the-loop

Confidence thresholds that hand uncertain cases to a person.

Monitoring & cost control

Per-workflow success rates, latency and spend.

How we deliver

You'll know where it stands every week.

01

Discovery

1–2 weeksScope document, risk list and a fixed estimate
02

Design & architecture

2–3 weeksClickable prototype and system design
03

Build

6–16 weeksA working demo at the end of every sprint
04

Test & harden

ContinuousAutomated test suite, UAT sign-off, security review
05

Launch

1 weekProduction deployment, monitoring and full handover
06

Support

90 days includedSLA-backed fixes and a roadmap for what's next

Engagement models

Pick the risk model that suits you.

Indicative starting points. We give a firm number after discovery — a fixed price quoted before we understand the scope is a number designed to be revised.

Fixed scope

₹8,00,000

starting from

A clear brief you want delivered to a firm budget

  • Fixed price agreed after discovery
  • Defined deliverables and milestones
  • Change requests quoted separately
  • 90 days post-launch support

Dedicated team

₹3,50,000 / month

starting from

Evolving requirements, or an in-house team that needs capacity

  • Senior engineers embedded in your standups
  • Scale the team up or down monthly
  • You set the priorities each sprint
  • Direct access — no account manager layer

Retainer

₹1,20,000 / month

starting from

Ongoing improvement, maintenance and support after launch

  • Agreed monthly hours
  • Guaranteed response times
  • Monitoring, patching and dependency upgrades
  • Quarterly roadmap review

FAQ

AI Automation questions.

Still have one? Talk to an engineer, not a salesperson.

With the highest-volume, lowest-judgement task you do — usually document processing or data entry between systems. Start with one workflow, measure it against the manual baseline, then expand. Programmes that begin with a strategy deck rarely reach production.

Not with the configurations we deploy. Enterprise API tiers from OpenAI, Anthropic and Google contractually exclude your data from training. Where that isn't sufficient — regulated data, or a client policy against any external processing — we run open-weight models in your own environment.

It depends on volume and model choice, but it's measurable and we instrument it from day one. A typical document-processing workflow costs a few rupees per document. We'll show you cost per run before you scale.

It will, occasionally — that's why confidence thresholds and human review exist. We design the failure path first: uncertain cases go to a person rather than through silently. Anything touching money or compliance keeps an approval step.

Traditional RPA follows fixed rules and breaks when a screen or format changes. AI-based automation handles variation — different invoice layouts, differently worded emails — which is where most real-world processes actually live.

Start the conversation

Tell us what you need.

An engineer replies within 4 business hours — with questions, not a brochure.

NDA available before you share anything.

A range is fine. It helps us scope honestly.

What are you building, what's the problem, and what does success look like?

An engineer replies within 4 business hours.