AI practice
AI that holds up in production
We design, build, and run AI systems on your data and inside your controls. Every engagement starts with a measurable business problem and ends with something your team can operate.
Capabilities
Six things we build and run, from choosing the use case to keeping it on budget.
Use-case selection
A ranked portfolio of AI opportunities scored on value, data readiness, and risk, so funding goes to the work that will pay back.
Data readiness and platforms
Pipelines, governance, and retrieval indexes that give models clean, permissioned access to the data they need.
Generative AI applications
Assistants for drafting, summarizing, and answering questions over policies, contracts, records, and manuals.
Agents and workflow automation
Agents that read, decide, and act through your APIs, with people approving the steps that carry risk.
Evaluation and quality
Test sets and scoring built from your real cases, run on every release, so accuracy is measured rather than assumed.
Operations and cost control
Monitoring, audit logs, model routing, and spend limits that keep a production system observable and on budget.
Where it pays off
Use cases in the industries we serve most. Each one starts with a work queue and a metric.
Insurance
- Claims intake triageRead first notice of loss documents, extract key facts, and route each claim to the right adjuster queue.
- Policy question answeringGive service teams cited answers from policy wordings and endorsements in seconds.
Healthcare
- Prior authorization preparationAssemble the clinical evidence a payer asks for and flag gaps before submission.
- Clinical document summarizationSummarize referral packets and discharge notes for care coordinators, with links back to the source.
Banking and financial services
- Know-your-customer reviewPre-check onboarding documents against policy and surface exceptions for an analyst to decide.
- Regulatory change trackingCompare new guidance against internal procedures and list the controls affected.
Transportation and logistics
- Shipment exception handlingClassify delay and damage events, draft customer updates, and open the right follow-up task.
- Freight document processingExtract data from bills of lading, invoices, and customs forms into your transport management system.
Utilities and energy
- Field work order assistanceGive crews equipment history and procedure steps for the asset in front of them.
- Customer contact deflectionAnswer outage and billing questions with account-aware responses and clean handoff to an agent.
Manufacturing
- Quality deviation analysisGroup nonconformance reports by likely cause and point engineers to similar past cases.
- Supplier document reviewCheck certificates and specifications from suppliers against your requirements before parts arrive.
How we work
Most AI programs stall between the demo and the day-to-day. We close that gap with engineers who have run production systems in insurance, healthcare, financial services, and logistics, and with a delivery method that measures accuracy, cost, and risk before anything goes live.
Frame
Agree the business metric, the baseline, and what good enough looks like before any model is chosen.
Prove
Build a pilot on your data and score it against the baseline with an evaluation set your team helped write.
Harden
Add access controls, guardrails, monitoring, and failure handling, then pass your security review.
Run
Operate the system with you, track accuracy and cost each month, and retrain or adjust as the work changes.
Platforms we work in
Cloud, data, and AI platforms our teams work in every day, and the kind of work we do on each.
OpenAI
Assistants, agents, and document intelligence on GPT models, with evaluation and guardrails designed in.
Anthropic
Claude models for long-document work, agents with tool use, and assistants where careful, auditable answers matter.
AWS
Landing zones, serverless and container platforms, data lakes, and migrations off aging infrastructure.
Microsoft Azure
Azure OpenAI, Fabric and Synapse data platforms, and .NET application modernization.
Snowflake
Governed warehouses, data sharing, and the modelling layer that reporting and AI depend on.
Databricks
Lakehouse platforms, PySpark pipelines, MLflow, and feature engineering at enterprise scale.
Apache Kafka
Event streaming between core systems so data moves in minutes rather than nightly batches.
PySpark
Large-scale transformation and machine learning pipelines in Python on Spark.
Guardrails
Controls that ship with every system, not added after an audit.
Your data stays yours
Models run in your cloud tenancy or under enterprise agreements that exclude training on your data.
People decide where it matters
High-risk actions require human approval, and every automated decision can be traced and reversed.
Measured before it ships
Every release is scored against an evaluation set, and accuracy thresholds are agreed with your risk owners.
Logged and auditable
Prompts, sources, outputs, and approvals are recorded so compliance teams can review what happened and why.