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We map the process, use a model only for the judgement calls that need one and automate the rest with ordinary code, so it runs cheaply and is easy to debug.
We map the process, find the judgement calls that actually need a model, and automate the rest deterministically. Cheaper, faster and far easier to debug.
We trace the real process step by step, including workarounds and spreadsheets, then mark which steps are rules and which genuinely need judgement.
Each candidate step gets an estimate of hours saved, build cost and running cost, so you automate in order of payback rather than novelty.
Workflows start from real events such as a new email, an ERP record or a webhook, and durable execution means a crash resumes rather than restarts.
We connect to SAP, NetSuite, Zoho, Tally, Salesforce, HubSpot and ServiceNow through their APIs, with retries and reconciliation on every write.
Emails, PDFs and scans are read, classified and turned into structured records, with confidence scores deciding what passes straight through.
Anything that fails a rule or falls below a confidence threshold lands in one queue with the reason shown, so people only handle real exceptions.
The aim is the cheapest reliable path through each step, which usually means far less AI than people expect and far more ordinary engineering.
We shadow the people doing the work, record every step, system and hand-off, and measure volumes and current turnaround from your own logs.
How workflow automation shows up across India, the Gulf and the US. Pick one to see what changes in the process.
Accounts staff match supplier invoices against POs and goods receipts in Tally by hand, often days after delivery.
Invoices are read on arrival, matched three ways against ERP records and posted, with mismatches queued with the reason shown.
Code, documentation and the tests that prove it — yours outright — and the limits it runs inside, agreed before anything goes live.
Each stage ends in something you can hold — a document, a demo, a passing eval, a dashboard. Nothing carries over on trust alone.
A paid two-week audit of your processes, data and systems. We come back with a ranked list of what AI should touch — and what it should not.
Audit report and ranked backlog
Model selection, retrieval design, guardrails and the integration surface. You get a written architecture with a cost model attached to it.
Architecture doc with cost model
Two-week sprints to implement agents, integrate with your systems and run internal evals. You see working software early and often.
A working demo in your environment
We run your real use cases, measure accuracy, latency and cost, and pressure-test edge cases with your team before go-live.
Evaluation report with KPIs
We help you launch, monitor and continuously improve. You get playbooks, dashboards and regular reviews to scale safely.
Live dashboards and runbooks
Still deciding?
Thirty minutes with an engineer who builds workflow automation. No deck, no discovery form.
Talk to an engineerTraditional RPA copies clicks on a screen and breaks when the screen changes. We integrate through APIs and events wherever possible, use ordinary code for rules, and use models only for reading unstructured documents or making narrow judgements. The result is less brittle and much easier to test and debug.
Most engagements combine two or three of these. Discovery is where we tell you which.
Agents that take a task, use your tools and finish it.
ExploreAnswers from your own documents, with the source cited.
ExploreModels tuned and tested on your task, not a benchmark.
ExploreGuardrails, tracing and access control around every model.
ExploreA costed plan of what to build, buy or leave alone.
ExploreBring one process you think an agent could run.
We'll tell you straight whether it's worth building — and what it would cost if it is.