AI · Workflow automation · Business process automation
Banao builds AI business process automation that handles the full case — reading the application, email, or scanned form that arrives, routing it through your approval chain, running the data checks against your systems of record, and closing the case without a person touching it for every routine instance. A person steps in only when the process surfaces a genuine exception, not because it hit a step the rule engine could not express.
We build it on the same foundations we use to run Banao's own 300-person operation: processes mapped before they are automated, decisions logged and replayable from day one, and every judgment step grounded in your data rather than a model's best guess.
Banao — Vikaas— our own demand-generation process runs as an automated workflow, decisions and all, every working day.
Automating a process is not installing a tool. It is assessment, design, decision engineering, system integration, and exception handling — we own the full scope.
Before a line of automation is built, we measure where the process spends its time, where errors occur, and which steps are deterministic versus which need reading or deciding — so we automate the right things rather than run a slow path at higher speed.
Emails, scanned documents, free-text forms, and PDFs converted into structured data the workflow can act on. This is the step where most rule-based automation breaks down — and where AI earns its place in the process.
An AI-driven routing layer that classifies each case, checks it against your thresholds and policies, and sends it down the right approval path — without a coordinator reading every ticket to decide where it goes.
Function calls wired to your core systems so the automated process reads live data, posts results, and updates records — including older systems via API retrofit. The workflow acts on real data, not copies.
A design that routes genuine edge cases to the right person with full context, not a raw error — so the team handles cases that truly need judgment and sees the rest finished automatically.
Every automated decision recorded with its inputs, the logic applied, and the action taken, so any case can be replayed and any audit trail reconstructed — essential for finance, HR, and regulated operations work.
Real-time visibility into case throughput, exception rates, and SLA breaches, with alerts that surface a stalled process before it creates a backlog rather than after the deadline has passed.
Cycle time, exception rate, and cost per case measured from the first live run and tuned on real data — so process improvement is a number on a dashboard, not an annual review.
Most buyers considering business process automation have a list of candidates. The list is rarely wrong about which processes are painful — it is often wrong about which ones are ready. A process can be high-volume and expensive and still be a poor automation target because it is not yet stable enough, because the inputs are too variable, or because the exception rate is so high that automating the happy path delivers almost nothing.
The processes that are genuinely ready share a pattern: high volume of similar cases, a meaningful share of routine instances that follow the same path, inputs that can be read and structured reliably, and a clear definition of a correctly finished case. When those conditions hold, end-to-end automation that closes routine cases without a person and routes real exceptions to one produces measurable time and cost savings from the first months in production.
A low-volume process with a large per-case cost can still justify automation. What it needs is stable enough steps to design a workflow around. If the process changes every quarter, the automation spends its time maintaining itself rather than saving time.
If 40% of cases are exceptions, straight-through processing covers 60% at best — and the exceptions may cost more to route than to have a person handle from the start. A Discovery Sprint uncovers this before a build budget is committed.
A process that arrives as a consistent API call or clean form field is far faster and cheaper to automate than one that arrives as a scanned PDF or a forwarded email chain. Knowing the input profile changes the build estimate significantly.
Not all business processes are equal candidates for automation. Finance, HR operations, and procurement consistently produce the highest early return because they combine volume, repetition, and a high share of inputs that can be read and acted on without a person in the loop for every case.
Finance operations — invoice processing, payment approvals, month-end reconciliation — typically run thousands of cases a month, most of which follow the same path. HR onboarding and offboarding run a smaller number of cases but carry a disproportionate error rate and delay cost from manual data entry. Procurement approvals — supplier qualification, PO routing, contract review — often have a well-defined policy that should be expressible as a routing rule but currently lives in someone's head.
Read incoming invoices from PDF or email, match against POs and contracts, route to the right approver, and post to your ERP — the routine cases run end to end, the discrepancies go to a person with context.
Move a new hire or departing employee through every step — document collection, system provisioning, cross-department notifications — with a single status view rather than chased email chains.
Route requisitions and POs through the right approval path based on amount, category, and supplier, and pull the checks from your compliance systems rather than asking a coordinator to chase them manually.
Metrics shown dotted (··) are being finalised in our case-study metrics pack — published only once independently verified. The deployments are live.
Banao — Vikaas
Banao's own demand-generation workflow plans, drafts, sequences, and routes outbound work with a person approving what goes out. We depend on it to run our own pipeline before we offer the pattern to a client.
Operations firm (anonymized)
An end-to-end process reads incoming requests, validates them against systems of record, routes approvals, and closes routine cases automatically — the team handles only the exceptions that fail a check.
Indian Oil
Long-running delivery of AI and automation work into a critical-infrastructure operation at national scale, built to hold up under the volume and governance that environment demands.
Banao is a ~300-person engineering company that runs its own business processes on the automation we sell. Vikaas manages our demand-generation workflow — planning, drafting, routing, and sequencing outbound work as an automated process. InterviewGod runs our hiring — screening applications and ranking candidates against a role before a recruiter opens the pile.
When an automated process has to survive our own operations, the version we hand to a client has already been tested against the messy inputs and unexpected edge cases a real business generates. That is a different kind of assurance from a vendor who has only built for others.
We would rather tell you this on the first call than deliver an automation that plateaus at sixty percent.
Most automation projects fail at the design stage, not the build. We prove which steps to automate, which to delete, and which belong to people — before committing a build budget.
We map your candidate process, measure where the time and errors go, test feasibility on the hardest steps, and hand back a scoped automation design, an exception plan, and an ROI model built on your real volumes and cost per case — yours to keep whether or not you continue. If you proceed, the Sprint cost is credited against the build.
We build the process workflow, the AI decision steps, the system integrations, and the exception-handling logic together — audit trail and compliance logging are deliverables, not afterthoughts.
We deploy behind approval gates with full monitoring, widen straight-through processing as the numbers allow, and keep cutting cycle time and exception rate on live cases.
What is business process automation?
Business process automation is wiring a repeated, multi-step process so it runs from trigger to close without a person intervening for every routine case. The steps can include reading inputs, checking data, routing approvals, updating systems, and sending notifications — the aim is a process that finishes on its own when the case is routine and pulls in a person only when it is not.
How is AI business process automation different from traditional BPA?
Traditional BPA works on structured inputs and deterministic rules — a clean form field triggers a fixed sequence. AI-driven BPA adds the steps that need reading or judgment: an incoming email, a scanned document, a request that does not match a fixed category. It is not a replacement for rule-based automation; it is the layer that handles what rules cannot.
Which business processes should we automate first?
High-volume processes with a high share of routine cases, inputs that can be read consistently, and a clear definition of a finished case are the strongest candidates — invoice processing, employee onboarding, procurement approvals, customer onboarding, and claims intake are common starting points. A Discovery Sprint maps your specific process and ranks candidates by ROI before committing a build budget.
What is the difference between BPA and BPM?
Business process management (BPM) is the practice of modelling, measuring, and improving processes — it is a discipline as much as a tool category. Business process automation is execution: building the system that runs the process without manual intervention. Good automation starts with the mapping and measurement BPM provides; the two are complements, not substitutes.
Can AI handle the unstructured inputs our processes receive?
Yes — that is the primary reason to add AI to a process. Emails, scanned PDFs, free-text forms, and photos can be read, extracted, and structured so the workflow can act on them. The accuracy of extraction depends on the consistency of the input format and the quality of available examples; a Discovery Sprint tests this on your real documents before a pipeline is designed.
How do you measure ROI from business process automation?
We measure cycle time, exception rate, and cost per case from the first live run. A baseline is established during the Discovery Sprint from your real volumes and current processing cost, so the improvement is a number you can show rather than an estimate made before the build. We report on these metrics continuously in production.
What happens when the automated process makes a mistake?
Every AI decision step carries a confidence score. Below the threshold, the case routes to a person rather than acting — so the workflow defaults to asking for help rather than acting on a low-confidence call. Every action is logged with its inputs and reasoning, so any case can be reviewed and corrected, and patterns of errors can be found and fixed without needing the original event to recur.
How long does it take to automate a business process?
A two-week Discovery Sprint produces the scoped design and ROI model. A first end-to-end workflow typically takes 6–10 weeks to build, depending on the number of integration points and the complexity of the AI decision steps. Deployment starts behind approval gates and widens as the exception rate falls. Banao's ~300-engineer bench means the build begins in weeks, not the months a fresh hire would take.
Bring the approval queue, the onboarding flow, or the invoice process your team still runs by hand. In 45 minutes we will tell you which steps to automate, what the exception plan looks like, and what it takes to run it end to end in production.
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