MSME & Micro-Lending
Automate credit decisions for unstructured evidence: handwritten ledgers, crumpled receipts, local IDs. Zero-shot document intelligence — no template training.
Where every decision must be correct, fast, and auditable. We turn "messy reality" — unstructured documents, images, logs — into deterministic outcomes using domain-trained SLMs deployed inside your VPC.
THE PROBLEM
When decisions are manual, the business cannot scale safely.
Documents, images, logs, exceptions. Unstructured evidence that legacy OCR can't handle without months of template training.
Brittle logic breaks under compliance. Policy exceptions compound. Manual overrides become the norm.
Slow, costly, inconsistent. Every new workflow means more headcount, more training, more risk.
"There is no infrastructure layer for regulated decisions. Until now."
THE SOLUTION
One engine. Many decision workflows. Every decision is explainable, traceable, and production-grade.
Domain-trained SLMs and VLMs interpret messy real-world evidence: documents, images, invoices, IDs, handwritten logs.
Zero-shot document intelligence. No template configuration. No 6-month training ramp-up.
Policy and business logic applied deterministically. Regulatory rules, risk thresholds, SOPs — enforced, not suggested.
Same input, same output, every time. No temperature variance. No hallucination risk.
Auto-approve, reject, or escalate to humans — with full justification and audit trail. Humans only handle true exceptions.
Every decision is explainable, reproducible, and compliance-ready.
Multi-modal engine: extracting deterministic decisions across image, video, text, and unstructured logs.
{
"evidence": "multi_modal",
"decision": "APPROVE",
"confidence": 0.99,
"latency_ms": 42,
"policy_flag": null
}WHY GENERAL AI FAILS HERE
WHERE IT PAYS OFF FASTEST
Automate credit decisions for unstructured evidence: handwritten ledgers, crumpled receipts, local IDs. Zero-shot document intelligence — no template training.
VLMs analyze storefront photos to verify business viability. Cross-reference physical imagery against EXIF geospatial metadata for fraud defense.
Apply underwriting rules to extracted data. Auto-approve, escalate, or reject with full auditable justification.
Interpret referrals, medical notes, and lab results using domain-trained SLMs. Apply payer and clinical policy rules to auto-approve or escalate.
Pre-screen claims using documents, images, and structured data to route only high-risk cases to human investigators.
Verify licenses, certifications, and compliance documents automatically during onboarding and audits.
All decisions are explainable, auditable, and run inside the customer's VPC.
ENTERPRISE GUARANTEES
The inference engine runs inside your secure cloud environment. No customer PII leaves your perimeter.
Powered by edge-based SLMs, Qwen-family VLMs, Rust, and low-latency GPU orchestration via vLLM. Backed by elite infra partnerships granting our team access to massive compute clusters, the newest GPUs, and frontier developer tools like Claude Code.
Ephemeral processing. Documents processed in-memory and instantly purged. Zero data retention.
Compliant with BSP IT Risk Management, Data Privacy Act, HIPAA, and EU AI Act requirements.
Designed for production, not pilots.
THE TEAM
Founder & CEO, BPOptima
Built and deployed decision infrastructure at Maya Bank — live in production. Domain expertise at the intersection of SLMs, regulated operations, and compliance. Previously scaled products to $200M ARR at Origo, built a $67M GMV commodity trading platform at Sauda Tech, and angel-invested in 18 startups (3 exits, 27% realized IRR).
NEXT STEP
We map one real workflow and show how it runs end-to-end without human bottlenecks.
Deployed inside customer cloud · Policy-constrained · Fully auditable