Solutions
Generative AI that reaches production.
Move past the demos. We design and build secure, governed generative AI, including assistants, agents, document intelligence, and enterprise search, that lifts productivity and stands up to real scrutiny.
Book a consultationThe problem
Hype is easy. Production is hard.
Most enterprises can build a convincing demo. Far fewer can put one into production, because that is where security, evaluation, grounding, and accountability stop being optional.
We start where those problems live, so generative AI becomes a capability the business can depend on rather than a demo that never ships.
What we build
Strategy through production.
Generative AI strategy
Where generative AI creates real value in your business, and where it does not.
Use-case prioritization
Funding the applications actually worth building, and stopping the ones that are not.
LLM application development
Production applications, not throwaway prototypes.
RAG architecture
Grounding models in your own trusted knowledge so answers can be traced.
AI agents
Systems that take action, safely and inside guardrails.
Prompt engineering and evaluation
Reliable output, and the evidence that it is reliable.
Use cases
Where it earns its keep.
- Knowledge assistants: instant answers from your internal documents, policies, and knowledge bases
- Proposal automation: first-draft responses grounded in past wins and current data
- Document summarization: contracts, reports, and filings distilled to what matters
- Customer-service copilots: the right answer surfaced at the right moment for every rep
- Code assistants: in-context suggestions and reviews trained on your codebase and standards
- Policy and contract search: semantic search across dense legal and compliance documents
Governance
Governance is not an afterthought.
Every solution we build carries the controls an enterprise actually needs. They go in from the first prototype, not the week before launch.
Responsible AI guardrails
Content policies and safety filters applied at every layer, not just at the interface.
Security and access control
Role-based access, data isolation, and audit logging from the first day.
Hallucination reduction
Grounding in your own sources, fact checking, and confidence scoring.
Rigorous evaluation
Automated test suites that measure accuracy against real cases before every release.
Ongoing monitoring
Drift detection and quality tracking in production, not only at launch.
Human review where it counts
Escalation paths for the decisions that should never be made without a person.
Stack
Built on the models and frameworks you trust.
Model choice follows the problem, the data, and your security posture, not a vendor relationship.
Foundation models
Orchestration
Deliverables
From roadmap to running application.
- A generative AI roadmap with prioritized use cases
- Prototypes and production applications
- AI agent workflows with guardrails
- Governance playbooks covering security, evaluation, and human review
- Adoption materials so the capability takes hold
Common questions
Questions we hear often.
How do you implement generative AI in the enterprise?
Strategy and use-case prioritization first, then production applications grounded in your own data, with governance built in from the first prototype rather than added before launch.
What is RAG?
Retrieval-augmented generation. The model answers from your documents rather than from memory, which makes answers traceable and cuts the failure mode where a system states something confidently and wrongly.
How do you keep it secure and accurate?
Access control that respects your existing permissions, grounding in trusted sources, evaluation against real cases before launch, monitoring afterwards, and human review anywhere the stakes justify it.
What is the difference between an assistant and an agent?
An assistant answers. An agent acts. The moment a system can take an action on your behalf, the governance question changes completely, which is why we treat the two differently.
Related
This work usually arrives through our AI Solutions practice. See how the practices and capabilities fit together on the services overview.
Next step
Build generative AI you can defend.
Book a consultation and we will find the use cases worth taking to production.
Book a consultation