Finance and ops should not live in spreadsheets at scale. We build rules engines, immutable event logs, and consoles that make exceptions a system problem — not an email thread.
Trusted by payment and lending ops under real volume
●Same-day recon for 95%+ transactions in comparable stacks
●Ops headcount held flat while volume scaled (~3× class patterns)
●Dashboards regulators and auditors can follow
Before → after
A clear picture of what changes when operations run on a system designed for your workflow — not generic SaaS defaults.
Before
Five bank files and still no EOD match
Chargebacks live in email
Nobody can explain why a row matched
After
Rules engine + exception queues with reasons
Immutable event log for money movement
Ops consoles with aging and SLA risk
Your 3-step system
Structured thinking you can repeat — diagnose the real process, build the product spine, then optimize with automation and intelligence.
1
Diagnose
We model instruments, files, matching rules, and your exception taxonomy.
2
Build
We implement ingestion, matching, journals, and investigation UX your ops trusts.
3
Optimize
We harden for scale: idempotency, replay, observability, and new product lines.
Case snapshot
Case snapshot — payments ops team at scale
Composite: volume tripled; headcount could not.
Same-day recon for 95%+ transactions after engine + queues
Ops stayed lean at ~3× prior volume
Exceptions became searchable — not forwarded threads
Composite; licensing and regulatory posture remain your responsibility — we implement controls you specify.
If this sounds familiar
Common pain signals
Finance exports five bank files and still cannot match UPI/card settlements by EOD.
Chargebacks and exceptions live in email, not in a system of record.
Underlying issue: Manual reconciliation, settlement delays, and weak audit trails.
Outcomes you can aim for
Numbers below align with our public case-study narratives — illustrative of strong execution in this problem class, not a guarantee for every engagement.
Mature recon often hits high same-day match rates and scales volume without linear headcount — like 95%+ same-day recon and flat ops headcount at 3× volume in our stories.
Typical stack & patterns
Go, PostgreSQL, React, Kafka (representative).
DirectionManual reconciliation, settlement delays, and weak audit trails. → tailored product and integrations.
Who this is for
Payment companies, NBFCs, and fintech product teams where reconciliation and settlement are advantages or requirements.
Why CPS TechLabs
Objections removed early — we are not here to sell a science project.
We do not rip everything out blindly
We integrate with what already works and replace modules only when the business case is obvious.
Built for real operations
Our UX and data models assume messy humans, partial data, and peak-hour pressure — not textbook workflows.
Fast delivery, phased rollout
We ship thin vertical slices early so stakeholders see real flows before we scale complexity.
FAQ
The questions buyers ask before they book time.
Are you a regulated entity?
No — we are your engineering partner. You own licensing, KYC/AML policy, and regulatory relationships.
How long does a first valuable slice take?
Most teams see a first production slice in roughly 2–6 weeks depending on scope, integrations, and approvals. Larger programs run in phased milestones — rarely a single big bang.
Do you provide ongoing support?
Yes. Many clients choose a monthly partnership or SLA-backed retainer after launch so you are not stuck maintaining alone.
Where are you based — can you work remotely?
We are headquartered in Prayagraj (Allahabad), Uttar Pradesh. We serve teams across Prayagraj, Lucknow, Kanpur, Varanasi, and the rest of India. Discovery and delivery are remote-first with scheduled on-site visits to UP cities when it helps.
Get your system blueprint
Tell us how you operate today — we reply with a concrete next step, not a generic brochure.