How We Typically Engage

Most successful AI initiatives follow a predictable path: align on business goals, validate value quickly, deliver production-ready implementation, then improve continuously.

1) AI Discovery Sprint (Fixed Scope)

Best for: teams exploring AI, looking for high-impact automation opportunities, or needing a clear plan before investing in implementation.

• Duration: typically 1–2 weeks
• Outcome: a prioritized roadmap + architecture recommendation
• Includes: workflow discovery, data & systems review, risk assessment (security/compliance), success metrics definition
• Deliverables: scope document, solution design, implementation plan, phased rollout strategy

Result: you get clarity, realistic scope, and a plan to move into a Pilot with confidence.

2) AI Pilot / MVP Build (Time-Boxed Delivery)

Best for: building your first AI assistant, workflow automation, or AI integration that delivers measurable ROI.

• Duration: typically 4–6 weeks
• Outcome: a production-ready pilot with monitoring and quality controls
• Includes: implementation, integration, validation, rollout plan, basic observability
• Deliverables: working solution, documentation, operational playbook, next-step scaling plan

Result: you don’t get a “demo” — you get something that can run with real users.

3) Monthly AI Retainer (Ongoing Engineering Capacity)

Best for: companies that want continuous improvements, stable delivery, and a long-term AI roadmap without hiring a full in-house team.

What a Retainer Covers

• New AI features and workflow automation
• Prompt / RAG improvements, quality tuning, evaluation loops
• Integration enhancements and reliability improvements
• Monitoring, cost optimization, and performance tuning
• Security hardening and operational support

Ways to Structure It

• Dedicated capacity: fixed monthly engineering allocation
• Priority backlog: your tasks prioritized and delivered continuously
• Ongoing optimization: quality, cost, and adoption improvements month-over-month

Result: predictable delivery rhythm, continuous value, and a partner that owns production readiness.

Optional Add-On: MLOps & Production Reliability

If your AI solution is already built (internally or by another vendor), we can help you productionize it.

• CI/CD for models and AI services
• Monitoring: latency, errors, drift, quality, and cost
• Release strategies: canary / blue-green
• Operational playbooks and incident readiness

What You Can Expect

• Engineering-first delivery: production-grade implementations, not prototypes
• Security by design: access control, logging, secrets handling
• Business outcomes: measurable impact and clear success metrics
• Transparent collaboration: clear scope, milestones, and communication