Services
Models, AI agents, and software — built by one team that owns the whole chain.
AI Agents & Automation
Most “AI transformation” advice is generic. We build AI where it actually pays: in the repetitive, document-heavy, and rule-laden parts of your operations.
What we build
- Document-processing agents — extract, classify, and route information from contracts, statements, applications, and correspondence; produce structured outputs your systems can act on.
- Research and monitoring agents — track sources (regulatory updates, market data, competitor activity) and deliver summarized, cited briefs on a schedule.
- Reporting and analysis agents — turn recurring analytical work (KPI pulls, exception reports, model monitoring summaries) into automated, auditable outputs.
- RAG knowledge agents — cited, verifiable answers grounded in your own documents and knowledge, not what the model happens to remember.
- Workflow automation — connect the AI to your existing tools: email, spreadsheets, databases, and internal systems, with human approval steps where decisions matter.
- Managed hosting — we host the agent and its entire infrastructure for you. No servers to build, no services to wire together, no maintenance to babysit — you get a working system, and we handle operations, updates, and monitoring.
- Local LLM configuration — private, on-premises model deployment for data that can’t leave your building: model selection, quantization, inference setup, and an honest read on when local beats the cloud.
The data foundation
Data is the foundation of an AI agent — an agent is only as good as the information it can reach. We build that layer before the agent, not after.
- Data consolidation — connect your siloed sources (databases, spreadsheets, document repositories, SaaS tools, internal systems) into one agent-ready foundation, with the pipelines to ingest, cleanse, and keep it current — so agents answer from the whole picture instead of a single file.
- Retrieval and indexing — chunking, embeddings, and vector stores that let agents search, retrieve, and cite your knowledge — the engine that powers RAG.
How we work with AI
We treat LLMs as components, not magic. Every deployment includes evaluation against your real inputs, guardrails and error handling, logging so outputs are auditable, and a clear definition of when a human must be in the loop. If a rule-based solution is cheaper and more reliable, we’ll say so — our job is the outcome, not the technology.
Data Science & Model Development
Our core practice. We develop and validate statistical and machine learning models with a focus on accuracy, transparency, and regulatory readiness — especially in mortgage, banking, and federal program contexts.
Model development
- Credit risk models — default, loss, and portfolio-level models from econometric foundations to modern machine learning.
- Prepayment models — including non-traditional portfolios such as student loans.
- Stress testing — CCAR and DFAST frameworks, with scenario design and results packages.
- Predictive and segmentation models — for pricing, portfolio management, and customer analytics.
Model validation
- Independent model review and validation for regulatory and internal governance needs.
- Full documentation: model choice, assumptions, data processing, optimization, test results, and implementation procedures.
- Ongoing monitoring design so models stay defensible after deployment.
Reporting and BI
- Dashboards and BI — automated reporting and real-time visualization of models and portfolios (Streamlit, BigQuery, Snowflake, and similar stacks).
Custom Systems & Business Logic
We build software around your specific business — your rules, your workflows, your logic — not off-the-shelf templates.
- Custom business systems — software built around your specific business needs and logic: pricing rules, approval workflows, and decision processes encoded into tools your team uses every day.
- Legacy modernization — SAS to Python and Spark conversion, preserving results while cutting maintenance cost.
- Model emulators and APIs — wrap models in fast, deployable services for production use.
- Data pipelines — custom ingestion, cleansing, and scheduled processing that keep downstream work reliable.
How engagements work
- Scoping call — you describe the problem; we assess fit, effort, and the honest limitations of any approach.
- Proposal — a fixed or phased scope with deliverables and timeline.
- Delivery — working software and models on a schedule, with documentation you can hand to your regulators or stakeholders.
- Handoff or partnership — we train your team, or stay on for monitoring and iteration.