AI & Automation
AI Automation & Agents
Agents that finish the work, not just draft it
- Process-first, not model-first
- Human review where mistakes are expensive
- Live in weeks, measured from day one
- 60-80%
- Manual touch time removed
- 4 weeks
- First workflow in production
- < 2%
- Escalation rate after tuning
Typical range across document, support and back-office workflows
Automation Sprint engagement
Measured on the evaluation suite we hand over
Where the value actually is
Most teams come to us wanting a chatbot and leave with something less glamorous and far more valuable: the invoice reconciliation that took two people three days a week, now taking one person two hours to review.
The pattern that works is narrow and deep. A single process, fully automated, including the awkward 15% of cases that the first demo skipped. The pattern that fails is broad and shallow — a pilot that handles the happy path, impresses a steering committee, and never survives contact with the exceptions.
What we build
Document and data workflows. Extraction, classification, validation and routing across invoices, claims, contracts, KYC packs and forms. Structured output with confidence scores, not prose that someone still has to read.
Conversational and support automation. Agents that resolve tickets by taking action in your systems — issuing the refund, updating the record, rebooking the shipment — rather than writing a polite summary of what a human should do next.
Internal copilots. Retrieval over your own documentation, policies and codebase, wired into the tools your team already has open.
Multi-step orchestration. Long-running processes that span systems, wait on humans, retry on failure, and can be audited afterwards. This is where most naive agent implementations fall over and where a durable workflow engine earns its place.
How we keep it honest
Every engagement produces an evaluation suite before it produces a demo. It is a set of real cases with known-correct answers, versioned alongside the code and run in CI. It tells you what the automation gets right today, catches the regression when a prompt change helps one case and breaks four others, and gives your team a way to keep improving it after we leave.
Cost per run is instrumented from the start. An automation that saves ₹40 of labour and burns ₹55 of inference is a science project, and we would rather find that in week two than at the first invoice.
How it runs
What the engagement looks like
Phases, not a proposal. Each one has an output you can see.
- 1
Map the process as it actually runs
Week 1We sit with the people doing the work. Documented process and real process differ, and automating the documented one is the most common way these projects fail.
- 2
Cut the scope to one measurable workflow
Week 1One workflow, one owner, one number that moves. Broad "AI transformation" scopes stall; narrow ones ship and then compound.
- 3
Build with evaluation from the first commit
Weeks 2-3We assemble a labelled test set before writing the agent, so accuracy is a number rather than an impression. Prompt and retrieval changes get measured, not argued about.
- 4
Ship behind a guardrail
Week 4Live traffic, with confidence thresholds routing uncertain cases to a human queue. Autonomy increases as the accuracy data earns it.
- 5
Hand over or keep running it
OngoingYour team takes it with documentation and training, or we operate it under a managed retainer. Both are fine; abandoning it in production is not.
Proof
Where we have done this
Freight forwarder and 3PL
Recovering 4.1% of freight spend by auditing every invoice
A mid-sized forwarder audited freight invoices by sampling roughly 5% of them, because auditing the rest by hand was uneconomic — so systematic small overcharges passed unnoticed, and shipment exceptions were routinely discovered when the customer rang to complain.
- 4.1%
- Freight spend recovered in year one
- 100%
- Invoices audited, from a 5% sample
- 74%
- Track-and-trace enquiries auto-resolved
Auto components manufacturer
Cutting quality documentation from 6 hours a shift to 90 minutes
Across three plants, quality engineers spent most of a shift writing non-conformance and inspection documentation by hand, while production reporting arrived a morning late because the MES, the historian and the ERP disagreed about what each machine was called.
- 6 hrs → 90 min
- Quality documentation time per shift
- 71%
- Non-conformance reports drafted automatically
- 1 shift
- Production reporting latency, from next morning
FAQ
Questions we get asked
How is this different from buying an off-the-shelf AI tool?
If a SaaS product already solves your problem, buy it — we will tell you so. We build when the process is specific to how your business works, when it spans systems that no single vendor integrates with, or when the data cannot leave your environment.
What happens when the model gets it wrong?
It will, so the design assumes it. Every automation has a confidence threshold, an escalation path to a named human queue, and an audit trail. We agree the acceptable error rate up front and instrument against it.
Do you need our data to train a model?
Almost never. Most business automation is solved with retrieval over your existing documents plus a well-specified agent, not fine-tuning. We will say clearly if a case genuinely needs training data.
Which processes are the worst candidates?
Anything with no consistent input format, no measurable output, or a genuine legal requirement for human judgement. We would rather rule those out in week one than bill you for discovering it in month six.
Talk to someone who does ai automation & agents
Thirty minutes with an engineer who has delivered this, not an account manager. You will get a straight answer on feasibility, rough cost and where it would fail.
Or email [email protected] · we reply within 1 business day