End-to-end support for teams that want AI done right
From strategy and governance through workshops, prototypes, and observability, the work is designed to build capability inside your team, not dependency on outside help.
Responsible AI by Default
Attribution, audit logging, and content safety are delivery requirements in every engagement, not add-ons.
Security and Compliance Aware
Experience in regulated environments means security posture, data sensitivity, and compliance requirements are always front of mind.
Your Team Owns the Outcome
Every engagement includes documentation, training materials, and a deliberate handoff. You should not need to call me again for the same problem.
Service details
GenAI Strategy and Roadmapping
Translate AI potential into a sequenced plan your team can actually execute.
Many AI roadmaps fail not because the technology is wrong, but because they are not anchored in the real constraints of the organization: what the team can actually build, what data exists, and what stakeholders will actually adopt. Strategy work here starts with discovery before any recommendations.
What this includes
- Capability audits to understand where you are and what is missing
- Use-case prioritization based on feasibility, impact, and risk
- Phased roadmaps aligned to your team structure and budget
- Vendor and tooling evaluation without a preferred-vendor bias
- Communication strategies for leadership and governance stakeholders
Responsible AI Governance
Accountability and compliance built into the delivery pipeline, not retrofitted later.
Responsible AI governance is often treated as a policy exercise. It works better as a delivery practice. That means defining evaluation criteria before building, adding attribution from day one, and running ongoing stakeholder loops rather than one-time reviews.
What this includes
- Attribution and audit logging frameworks for AI-generated outputs
- Content safety and guardrail design for production systems
- Ethical integration standards for analysts and engineering teams
- Outreach and consultation with mission owners and compliance stakeholders
- Published guidance and policy documentation for responsible use
Enablement Workshops
Hands-on learning designed around your tools, data, and the problems your team actually has.
Generic prompt engineering tutorials do not change how teams work. Workshops that start with your team's specific problems, use your actual data, and leave participants with a documented workflow they can run on Monday morning, those do.
What this includes
- Python and SQL workshops for analysts and engineers
- Responsible AI and ethical use training for technical and non-technical teams
- GenAI literacy programs for product, UX, and leadership audiences
- Custom curricula built around your specific domain and tooling
- Follow-up materials, reference guides, and repeatable workflow templates
Prototype and Pipeline Development
Working prototypes that demonstrate what is possible and are built to scale.
Prototypes are most valuable when they are honest about their constraints. A working NL-to-SQL interface with documented failure modes and a real evaluation layer teaches your team more than a polished demo that only works on curated inputs.
What this includes
- NL-to-SQL pipelines with schema-aware generation and access controls
- Retrieval-augmented generation (RAG) systems with reliable grounding
- Training data tools and annotation pipelines
- Demo environments and proof-of-concept systems for stakeholder review
- Handoff documentation and code that your team can maintain
Observability and Evaluation
Know what your AI systems are actually doing, where they fall short, and why.
You cannot improve what you cannot observe. Observability infrastructure, built at the time of deployment rather than retrofitted later, is the difference between AI systems that improve over time and ones that silently degrade.
What this includes
- Usage and performance dashboards for deployed AI systems
- Adoption tracking across teams and user segments
- Evaluation frameworks with structured quality criteria
- Error and edge-case analysis for production systems
- Reporting for leadership and compliance stakeholders
NL-to-SQL and Data Access
Language-to-data interfaces that are safe, accurate, and production-ready.
NL-to-SQL is one of the most useful GenAI applications for data teams and one of the most fragile. The model is just one component. The schema context, access controls, evaluation layer, and observability infrastructure matter just as much.
What this includes
- Schema-aware SQL generation with curated context and access controls
- Evaluation layers to catch silent incorrectness before users do
- Role-based schema filtering to enforce data governance rules
- Monitoring and feedback loops for continuous quality improvement
- Training materials so your team understands and can extend the system
Not sure which service fits?
Start with a call. We will figure out together what your team actually needs and what the right starting point looks like.