Financial Services
Risk, compliance, and secure automation for regulated workloads.
Solutions we deliver
Intelligent assistants & copilots
Document AI & knowledge search
Anomaly & fraud detection
Predictive maintenance
Computer vision at the edge
Data platforms & lakehouses
MLOps and governance
Secure offline AI deployments
Industries
Healthcare
PHI-aware systems with strong privacy and reliability guarantees.
Public Sector
Mission-critical solutions built for security and accountability.
Manufacturing
Quality, safety, and predictive maintenance with edge AI.
Energy
Optimization, forecasting, and resilient operations.
Retail
Personalization, logistics, and demand planning.
How we work
1. Discovery
Align on objectives, data, and metrics with security addressed from day one.
2. Design
Architecture and model approach tailored to cloud, on-prem, or edge.
3. Build
Iterative delivery with testing, evaluation, and clear documentation.
4. Deploy
Operationalization with MLOps, monitoring, and secure rollout.
Security & Compliance
Confidential by Design
Data minimization, encryption in transit and at rest, least-privilege access.
Governance Ready
Support for SOC 2, ISO 27001, HIPAA, and sector-specific obligations.
Offline Capable
Operate securely without continuous connectivity, including air-gapped.
Use cases
What we actually build
Concrete systems we’ve shipped. Every example below started as a sketch on a whiteboard and ended as a running production service.
Intelligent assistants & copilots
Private LLM assistants grounded on your documentation, policies, and historical decisions. Every answer cites the clause or page it came from — the feature we consider non-negotiable for regulated environments.
Typical timeline: 6-10 weeks from scope to pilot.
Document AI & knowledge search
Turn thousands of policies, manuals, or research documents into a searchable, answerable knowledge base. Hybrid retrieval (BM25 + dense) plus reranking for accuracy on both exact and semantic queries.
Typical timeline: 8-12 weeks depending on corpus complexity.
Anomaly & fraud detection
Real-time transaction, access-log, or sensor-stream anomaly detection. Explainable outputs so fraud operations and compliance teams can defend the decision to regulators.
Typical timeline: 10-14 weeks to production with ongoing tuning.
Predictive maintenance
Time-series models over sensor telemetry for manufacturing, utilities, and fleet operations. Early-warning alerts at asset-level with confidence scoring for operator trust.
Typical timeline: 12-16 weeks including edge deployment.
Computer vision at the edge
On-device inference for quality inspection, safety monitoring, and throughput measurement. Optimized models run under strict latency and power budgets without cloud dependency.
Typical timeline: 8-14 weeks, hardware-dependent.
Data platforms & lakehouses
Production lakehouse architecture on Databricks, Snowflake, or open-source stack (Iceberg + Trino). Governance, lineage, and observability wired in from day one.
Typical timeline: 12-20 weeks for initial platform; ongoing for expansion.
MLOps and model governance
Model registry, evaluation harness, drift detection, and promotion workflows. Bring existing notebooks into a versioned, tested, reproducible lifecycle.
Typical timeline: 6-10 weeks to establish; continuous from there.
Secure offline AI deployments
End-to-end offline AI stack — parsing, embeddings, vector store, inference, orchestration — all deployed inside your perimeter. No external calls, no telemetry, no surprises.
Typical timeline: 10-14 weeks including compliance review support.