AI that works in regulated industries
We build AI and ML systems that satisfy compliance requirements and actually ship to production. Not demos — deployed, monitored, audit-ready software for fintech, healthtech, and enterprise.
What we build
Six capabilities across the AI/ML stack — each built for industries where mistakes have regulatory consequences.
AI Agents & Chatbots
Customer support, onboarding, and sales pipeline automation. Conversational AI that handles real queries with guardrails and escalation paths built in.
ML Contract Management
Document extraction, classification, anomaly detection, and obligation tracking. Turn unstructured contracts into structured, actionable data.
Compliance Automation
Regulatory monitoring, risk scoring, and automated reporting. Stay ahead of changing rules without throwing more bodies at the problem.
Predictive Analytics
Churn prediction, fraud detection, and credit scoring models that run in production — not just notebooks. Monitored, versioned, and explainable.
NLP & Document Processing
Entity extraction, sentiment analysis, and document classification. Process thousands of documents with the accuracy regulators expect.
Computer Vision
Identity verification, document scanning, and damage assessment. Visual AI that integrates into existing workflows and meets audit requirements.
Why it's different in regulated industries
AI in fintech and healthtech isn't just harder technically — it's harder legally, operationally, and reputationally. Here's what most AI shops get wrong.
Data privacy by design
Every model touches sensitive data — PII, financial records, health information. We architect for data minimization, encryption at rest and in transit, and jurisdictional compliance from the start.
Audit trails on everything
Regulators want to know what the model decided, when, and why. We build comprehensive logging and versioning so every prediction and action is traceable.
Explainability, not just accuracy
A black-box model that scores 99% is useless if you can't explain its decisions to a compliance officer. We select and tune models with interpretability as a first-class requirement.
Model governance & drift detection
Models degrade. We implement monitoring pipelines that catch data drift, performance decay, and distribution shifts before they become regulatory incidents.
Machine Learning for Contract Management
Contracts are the worst kind of unstructured data — long, dense, inconsistent in format, full of obligations that affect revenue, liability, and compliance. Most companies still manage them by paying lawyers and ops teams to read them. ML changes the economics: extraction, classification, and obligation tracking that used to take a paralegal a week now happen in minutes.
We build ML contract management systems that ship to production and meet the compliance bar regulated industries demand — audit-logged, explainable, integrated with the rest of your stack. Four use cases we have shipped repeatedly:
Document Extraction & Classification
Pull parties, dates, payment terms, jurisdictions, and obligations out of contracts in any format — PDFs, scanned documents, Word files. Classify by type (MSA, SOW, NDA, lease, employment) so downstream workflows route correctly.
Obligation Tracking
Turn contractual obligations into structured, queryable records — payment milestones, SLA commitments, audit windows, renewal triggers. The system surfaces what is due, when, and to whom, instead of leaving it buried in PDFs.
Anomaly & Risk Detection
Flag clauses that deviate from your standard playbook — unusual indemnity, exotic governing law, non-standard liability caps. Compare incoming third-party paper against your approved templates and surface what a lawyer needs to look at.
Renewal & Expiry Intelligence
Auto-detect renewal windows, notice periods, and auto-renew clauses across your portfolio. The system tells you which contracts are quietly auto-renewing next quarter, which need renegotiation, and which can be let expire.
Going deeper: ML-Powered Contract Management: When to Build, When to Buy — a practical breakdown of what works, what doesn't, and how to decide.
Proof it works
Real AI/ML systems we've built and deployed for regulated companies.
Encore Compliance
AI compliance platform — 85% cost reduction
Replaced a manual compliance workflow with an AI-powered platform that processes regulatory documents in seconds instead of hours.
Sokin
AI support agent — 60%+ queries resolved autonomously
Built a conversational AI agent for a global payments company that handles the majority of customer support without human intervention.
ML contract management — frequently asked
What founders and ops leaders typically ask before building an ML contract management system.
Can machine learning replace manual contract review?+
For routine work, yes — extracting parties, dates, payment terms, and standard obligations is a solved problem with modern ML and LLMs. For high-stakes negotiation work, no — ML augments lawyers, it does not replace them. The right framing is: ML handles the 80% of contract review that is repetitive, freeing your legal team to focus on the 20% that needs human judgment.
What does an ML contract management system actually do?+
It ingests contracts (PDFs, scans, Word files), extracts structured data (parties, dates, financials, obligations, clauses), classifies them by type, compares them against your standard playbook, surfaces anomalies for review, and feeds the structured output into your downstream systems — CRM, ERP, compliance dashboards. The contract becomes searchable, queryable, monitorable data instead of a static document.
How long does it take to deploy an ML contract management system?+
Most production deployments ship in 8 to 16 weeks depending on scope and integration depth. The core extraction pipeline ships in 4 to 6 weeks. Custom clause classification, integration with existing systems (Salesforce, NetSuite, document storage), and human-in-the-loop review workflows take the rest. We are faster than traditional vendors because we build on modern LLM infrastructure rather than fine-tuning legacy NLP pipelines.
Do we need our own labeled training data?+
Not necessarily. Modern LLM-based extraction works well out of the box for standard contract types and clauses. Where labeled data helps is when you need to detect company-specific clause patterns, classify against your particular playbook, or automate decisions that touch your business rules. Even then, we can usually start with a few dozen labeled examples rather than the thousands legacy ML required.
What is the difference between a contract management tool and an ML contract management system?+
A contract management tool stores contracts and lets you search and version them. An ML contract management system reads them — extracts the meaning, structures the obligations, flags the risks, and feeds that structured information into the rest of your stack. The first is a filing cabinet with search; the second is software that actually understands what is in your contracts.
Will the system meet our compliance requirements?+
We build ML contract management systems for fintech and regulated industries by default — audit logs on every prediction, role-based access controls, encryption in transit and at rest, data residency controls, and explainable extraction so legal can verify why the model made a given call. Productera is ISO 27001 certified and we ship SOC 2-aligned infrastructure as a default.
Let's talk about your AI project
Book a free 30-minute call. We'll discuss your use case, your compliance requirements, and whether AI is actually the right solution.