AI strategy & use cases
Decide what is worth building before anything is built.
- Use case discovery
- Value and feasibility scoring
- Data readiness review
- Target architecture
- Delivery roadmap
Enterprise AI
Move enterprise AI from experiment to production.
TekLid builds AI assistants and agents on your own Microsoft data — inside your Azure tenant, under your identity model, with answer quality and cost measured from the first week.
- Runs inside your own Azure tenant
- Permission-trimmed retrieval by default
- Quality and cost measured, not assumed
What we build
The same pattern, applied to your use cases.
Entra ID identity, permission-trimmed retrieval, models inside your tenant, and telemetry from day one.
RAG & enterprise search
Retrieval that respects the permissions of the person asking.
- Azure AI Search
- Document processing pipelines
- Permission-trimmed retrieval
- Grounding and citations
- Evaluation harness
Agents & applications
Assistants that take action in your systems, with a human in the loop where it matters.
- Azure OpenAI
- Tool-calling agents
- Workflow automation
- Application integration
- Human-in-the-loop design
AI governance & operations
The controls that let a solution stay in production once it is there.
- Responsible AI policy
- Prompt and model versioning
- Quality evaluation sets
- Usage and cost telemetry
- Model lifecycle management
Outcomes
What separates a pilot from a production system.
Answers users trust
Responses cite the source document and respect the permissions of the person asking.
A path past the pilot
Solutions are built on the same landing zone, identity model and CI/CD as the rest of the estate.
Measured quality
Evaluation sets make regressions visible before your users are the ones reporting them.
Controlled unit economics
Token, model and retrieval costs are attributed per use case from the very first deployment.
Deliverables
What you receive.
A working solution in your tenant, plus everything needed to run and extend it.
- Prioritized use case portfolio
- Reference architecture and threat model
- Working solution deployed in your tenant
- Evaluation set and quality baseline
- Cost and usage dashboard
- Operating model and handover
Ready to build a secure, AI-ready Microsoft cloud?
Start with a focused assessment of your cloud, security, AI or FinOps environment.