SHRASIT Solutions
Services · RPA & Automation

We design, build and run automation programs that hold up under month-end load, regulator scrutiny and the real-world chaos of multi-system back offices. Multi-platform RPA, blended with applied AI where judgment matters, governed like the production systems they feed.

Bot run · AP-INV-042

Live
  1. Invoice arrivesEmail / EDI / portal
  2. OCR & extractHeader, lines, tax
  3. Validate & matchPO + GRN + vendor
  4. Post to ERPSAP / Oracle / D365
  5. Reconcile & closeAudit trail + KPI
Cycle
38s
Today
2,184
Exceptions
0.6%
Bots in production
0+
Hours returned / year
0
FTE equivalents reclaimed
0
Pipeline reliability
99.4%

The ROI we underwrite

Two columns. The same back office, before and after.

The numbers below are the median across our last forty programs in finance, banking and healthcare ops. We’ll baseline yours in discovery before we put a number on the proposal.

Before automation

  • Manual handling

    12 to 18 hours per analyst, per week, on copy-paste between systems that should already talk.

  • Error rate

    2 to 4 percent of postings get reversed in month-end. Each reversal costs ninety minutes of senior time.

  • Audit risk

    Sampled trails. Screenshots. WhatsApp approvals. Auditors find the seams every year.

  • Throughput ceiling

    Volume only grows by hiring. Year-end peaks lead to weekend overtime, not capacity.

After SHRAS automation

  • Lights-out processing

    Eighty to ninety-five percent of high-volume transactions complete without a human, including evenings and weekends.

  • Sub-1 percent error rate

    Validated by control rules and exception queues, not faith. Rejects route to a named owner with SLA.

  • Audit trail by default

    Every keystroke, decision and approval logged with timestamp, user and source document. SOX, ICFR, GDPR ready.

  • Linear cost to scale

    Volume doubles, headcount does not. Add bot capacity, not seats, to absorb peaks.

Use cases in production

Six domains. Each one is a real customer pattern, not a slide.

  • Banking & compliance

    01

    KYC, AML & reg reporting

    • Customer onboarding with sanctions and PEP screening
    • AML transaction monitoring with case packaging
    • Regulatory reports lifted from core banking nightly

    UiPath · Automation Anywhere

  • Finance ops

    02

    AP, AR & reconciliations

    • Three-way match on PO, GRN and invoice with exception queue
    • Bank, intercompany and GL reconciliations end-of-day
    • Journal posting and accruals with supporting evidence pack

    UiPath · Power Automate

  • HR & payroll

    03

    Onboarding & lifecycle

    • Joiner-mover-leaver across HRIS, AD, payroll and benefits
    • Payroll input consolidation, validation and run preparation
    • Timesheet ingestion, leave accrual and statutory filings

    Power Automate · Blue Prism

  • Healthcare ops

    04

    Claims, pre-auth & coding

    • Insurance pre-authorization across payer portals
    • Claims submission, follow-up and denial management
    • Discharge coding handoff to ICD-10 and CPT routines

    UiPath · OpenAI (assist)

  • Insurance

    05

    Underwriting & policy ops

    • Submission triage and risk-document extraction
    • Policy issuance, endorsement and renewal across LOBs
    • FNOL intake, fraud signals and segmented routing

    Pega · Automation Anywhere

  • Supply chain

    06

    Order, freight & supplier

    • Order entry from EDI, email and customer portals
    • Freight invoice audit with carrier-rate reconciliation
    • Supplier onboarding, master-data hygiene and quote chasing

    UiPath · Python custom

Cognitive automation

Where a deterministic bot ends, the AI layer begins.

Pure RPA is a power tool for stable, rule-bound work. The interesting back-office isn’t stable: invoices arrive as scans, claim notes arrive as paragraphs, and approvals require judgment. We blend both, cleanly, so the bot still owns the transaction and the AI owns only the messy edge.

Deterministic bots

Rule-coded, audit-friendly, built for high-volume, repeatable transactions. Same input, same output, every time, with a logged decision tree behind it.

  • Three-way invoice match
  • Bank reconciliation
  • Master-data hygiene
  • Statutory filing extraction

AI-augmented decisioning

OCR for scanned documents, NLP for unstructured notes, and LLM-assisted classification for fuzzy categorization. Outputs feed the bot with confidence scores; below threshold, a human decides.

  • Document understanding (OCR + LLM)
  • Email and case-note triage
  • Free-text to structured fields
  • Exception summarization for reviewers

The split rule

Bots own the transaction. AI owns the interpretation. Humans own anything outside tolerance. Every decision is logged with its source, model version and confidence, so audit can replay the call months later.

Tooling bench

We’re tool-agnostic by policy. The estate decides.

Certified developers and architects on every major platform, with a shared component library that travels. If your shop already runs one of these, we extend it. If it doesn’t, we’ll recommend based on volume, integration surface and audit posture.

Primary

Default

Where most programs land

  • UiPathDefault for unattended at scale, Orchestrator + AI Center
  • Automation AnywhereBanking and insurance estates with A360
  • Power AutomateMicrosoft 365 / Dynamics-anchored shops

Secondary

Picked when the estate calls for it

  • Blue PrismMature CoEs with strict change control
  • PegaCase-managed and BPM-heavy workflows
  • Python customHeadless integrations and bulk data work

AI layer

Cognitive augmentation

  • OpenAIDocument understanding and structured extraction
  • AnthropicDecisioning copilots, long-context review
  • Hugging FaceSelf-hosted NLP for regulated estates

Governance & ops

Bots that don’t go quiet on month-end.

A bot in production is a piece of operations infrastructure. Treated that way from day one, automation programs scale. Treated as a project that ended at go-live, they decay quietly until the next quarterly close exposes them.

Center of excellence

Operating model, intake pipeline, reuse library, citizen-developer guardrails and a quarterly review cadence that an executive sponsor will actually attend.

Bot monitoring

Bots that don’t go quiet on month-end. SLAs, queue depth, run-time drift and exception rates piped into Datadog or Splunk, with paging that wakes a human only when it should.

Change control

Versioned bot packages, tested in dev and UAT against masked production data, promoted on a release calendar that matches the underlying app’s patch window.

Audit trails & secrets

Every step logged with user, document and timestamp. Credentials in vaults (CyberArk, Azure Key Vault, AWS Secrets Manager). Segregation of duties enforced in code.

Engagement model

Discover. Build. Run. Each phase has a deliverable a CFO will sign.

  1. Phase 012 to 4 weeks

    Discover

    Process inventory scored on volume, complexity, stability and ROI. Top-twelve shortlist, candidate architecture, business case signed off by finance.

    Success criteria

    A ranked pipeline a CFO can defend in a steering committee.

  2. Phase 026 to 12 weeks per wave

    Build

    Bots designed, built, tested and hyper-cared into production. Reusable components, exception handling, monitoring, and runbooks written by the team that built them.

    Success criteria

    Bots in production hitting target volume with sub-1 percent error.

  3. Phase 03Ongoing, quarterly cadence

    Run

    Managed bot operations, change control, regression testing on host-app upgrades, and a roadmap of new processes pulled from the discovery backlog.

    Success criteria

    Every quarter ends with more capacity than it started with.

Sectors served

Where automation pays for itself in the first quarter.

Every sector chip below has a live program reference and a named practice lead who has shipped the work, not just sold it.

Frequently asked

The questions buyers actually ask in the first call.

Are we locked into the RPA vendor you pick?

No. We document the process logic separately from the bot implementation, in a tool-agnostic format. Migrations between UiPath, Automation Anywhere, Power Automate, Blue Prism and Pega are part of our practice, not an emergency project.

Who owns the bots once you leave?

You do. Source code lives in your repos, secrets in your vaults, runbooks in your wiki. We can stay on as a managed-services partner if that’s the model you want, but the IP and the operating capability transfer at go-live.

When is RPA the wrong answer?

When the underlying system has a clean API and the volume justifies it, automate the integration, don’t screen-scrape it. When the process changes weekly, fix the process before automating chaos. We will tell you to refactor the source rather than wrap brittle bots around it.

RPA versus AI agents, how do you decide?

Deterministic bots for high-volume, rule-based, audit-sensitive work. Cognitive layers (OCR, NLP, LLM-assisted decisioning) for unstructured inputs and judgment calls. We almost always blend the two: the bot owns the transaction, the AI augments the messy edges, and a human approves anything outside tolerance.

What does the security model look like?

Bots run as service accounts, credentials never touch the bot script, secrets resolve at runtime from a vault. Recordings and screenshots are masked. SoD enforced through Orchestrator roles. Penetration testing on the orchestrator and an ISO 27001-aligned operating posture.

Adjacent practices

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