Industries

Built for organizations where trust matters

The strongest fit is teams with real sensitivity around client data, internal knowledge, operational controls, or regulatory expectations — and an internal owner ready to make the deployment work.

Sector detail

Where the work most often lands

Each sector has its own data-handling reality. The questions below are the ones that come up before architecture work begins.

Payments and fintech

Internal AI for payment processors, fintech operators, and financial services teams that handle sensitive transaction, customer, and compliance information.

Common asks

Internal policy and procedure assistants
Operational playbook search across runbooks and incident records
Compliance evidence and obligation lookup
Vendor and counterparty due-diligence support

Constraints designed in

PCI scope — cardholder data must stay out of LLM context windows
BSA/AML evidence handling and retention obligations
Regulator expectations around explainability and access logging
Tight identity boundaries between operations, risk, and engineering

Healthcare-adjacent teams

Use cases where confidentiality, auditability, and data-handling discipline matter more than novelty — typically operations, administration, or research-adjacent functions rather than clinical decision-making.

Common asks

Documentation and intake workflows
Knowledge retrieval across SOPs and clinical reference content
Controlled summarization of internal records
Vendor and contract review support

Constraints designed in

PHI handling boundaries and the BAA chain
HIPAA-adjacent retention and minimum-necessary principles
No clinical decision support outputs without explicit medical-device review
Clear separation between deidentified data and identified workflows

Legal and compliance

Private AI for firms and in-house teams that need strong confidentiality expectations, reviewability, and deliberate access controls — typically internal research, drafting support, and obligation tracking.

Common asks

Document analysis and clause comparison
Research assistance over internal precedent and templates
Policy and obligations lookup across regulatory libraries
Matter intake and conflict screening support

Constraints designed in

Privilege and confidentiality boundaries between matters
Conflict walls implemented at the retrieval layer, not just the application
No unreviewed legal advice output to non-lawyer end users
Audit-grade logging of who queried what content

Professional services

Advisory and delivery teams that want internal AI capability without putting client material into unmanaged public tools — typically consulting, accounting, and specialized advisory practices.

Common asks

Internal knowledge base assistants
Proposal and RFP support drawing from prior engagements
Internal research workflows across regulatory and industry sources
Engagement playbook lookup and onboarding support

Constraints designed in

Client-confidentiality and NDA obligations enforced at the data layer
Strict separation between engagements and practice areas
Defensible retention and destruction policies for client material
Identity boundaries that match billing and engagement scoping

Pattern across sectors

What the strongest-fit engagements share

Industry differs. The signals that the work will succeed do not.

A named use case

One workflow, one team, one set of acceptance criteria — not "explore AI."

Real data sensitivity

Something concrete that public tools cannot be used for.

An internal owner

A person responsible for validation, adoption, and post-launch operation.

Security at the table

Security or compliance is involved early — not as a gate at the end.

What we do not take on

Sectors and use cases that are not a fit

The practice is intentionally narrow. Flagging this early avoids wasted discovery conversations on both sides.

Consumer-facing chatbot products without an internal sponsor and security owner
Open-ended "AI strategy" work without a candidate use case
Clinical decision support, regulated medical-device output, or anything requiring FDA clearance
Pure model training research without a deployment endpoint
High-volume content generation for marketing or advertising programs
Engagements where leadership wants a demo but is unwilling to fund the controls behind it

Sector not listed?

Industries above are not exhaustive. If you operate somewhere trust-driven that is not listed — insurance, asset management, regulated SaaS, public-sector adjacent — the conversation is still worth having.

Book discovery Security and governance