Unitforce Technologies
Consulting Pvt Ltd
Whole processes automated end to end, with the exceptions routed to people instead of hidden from them.
Intelligent automation of business processes start to finish — onboarding, payroll orchestration, claims and data reconciliation — removing manual handoffs and error while keeping a clean audit trail.
Automating a broken process makes it fail faster. We start by mapping the real one — including the spreadsheet step nobody documented and the approval that happens over chat — and then decide what should be removed, what should be automated, and what genuinely needs a person.
That map is also the business case. It shows where the handoffs, the rework and the waiting are, which is usually a more honest guide to where automation pays than a list of tasks people say they dislike.
Classical automation handles structured, deterministic steps well. It struggles the moment an input is an email in free text, a scanned invoice with a layout it has not seen, or a case that needs judgement. That is the part AI takes: classification, extraction, matching and summarisation over messy inputs, feeding the deterministic steps either side of it.
Runs are both scheduled and event-driven, so a process can be a nightly batch, a reaction to a webhook, or both, without being rebuilt for each.
No automated process is right one hundred per cent of the time, and the dangerous designs are the ones that pretend otherwise. Everything we build has an exception queue: cases the system is not confident about are held, surfaced with the reason, and routed to the person who can settle them.
Metrics come as standard — volume processed, exception rate, time saved, cost per run — because a process nobody measures quietly degrades, and an audit trail of every change is what keeps the whole thing defensible later.
RPA repeats a fixed sequence of deterministic steps. AI automation handles the parts RPA cannot — reading unstructured documents and email, classifying, extracting, matching and judging — and hands the structured result back to the deterministic steps around it. In practice the two are used together.
The ones that are high volume, rules-heavy, and currently held together by manual handoffs — onboarding, payroll orchestration, claims handling and data reconciliation are common starting points. Process mapping is what identifies the right first candidate in your own organisation.
The case goes to an exception queue with the reason it was held, and a person decides. Confidence thresholds are set deliberately, so uncertainty surfaces as a review task rather than as a silently wrong outcome.
Yes. Every run, decision and change is logged, so any individual case can be reconstructed after the fact — which is normally a requirement wherever the process touches payroll, statutory filings or customer money.
Agents that do the work, not just describe it — wired into the systems your team already uses.
Generative AI that answers from your facts, cites where they came from, and says nothing when it has no evidence.
An assistant inside the tool people already have open, grounded in what your company actually knows.
Fraud flagged as it happens and credit scored at the point of decision — with reasons an auditor will accept.
The unglamorous half of AI — versioning, serving, monitoring and rollback — owned by us so your team doesn't carry it.