Generative AI Implementation Services
The gap between demo and production is where programs stall. We close it.
Request an Implementation ReviewThe Five-Phase Implementation Process
Every GenAI implementation engagement follows a five-phase process designed to move you from scoped use case to production system — without skipping the governance work that makes production deployment defensible.
Discover
Define the workflow, the data, the integration points, and the success criteria. Most GenAI implementation failures are rooted in insufficient discovery — teams build the right model for the wrong problem because they did not spend enough time understanding the actual workflow.
What happens in this phase
- Workflow documentation and failure-mode analysis
- Data inventory and access assessment
- Integration point mapping across relevant systems
- Success criteria definition in measurable business terms
- Stakeholder alignment on scope and expectations
Design
Architecture, model selection, guardrails, and human-in-the-loop design. This is where vendor-agnostic thinking matters most — the right model for your use case may not be the most popular or the most heavily marketed one.
What happens in this phase
- Model selection across Claude, GPT-4, Gemini, Llama, and specialized alternatives
- System prompt and guardrail architecture
- Human-in-the-loop checkpoint design
- Integration architecture for enterprise system connectivity
- Output validation and quality gate design
Pilot
Build the minimal viable version in your actual environment — not a sandbox, not a demo, not a proof-of-concept that will need to be rebuilt before deployment. Real environment, real data, real users, contained scope.
What happens in this phase
- Minimal viable implementation in production environment
- Internal user testing with structured feedback collection
- Edge case and adversarial input testing
- Performance benchmarking against baseline workflow
- Iteration based on real-user feedback, not assumptions
Govern
Security review, compliance check, monitoring setup, and escalation paths — before any rollout. Governance added after deployment is governance that will never be right-sized. It needs to be built into the system design.
What happens in this phase
- Security review of data flows and model access patterns
- Compliance check against applicable regulations
- Monitoring and alerting setup for model performance and failures
- Escalation path design for edge cases and failures
- Documentation and model card creation
Scale
Rollout plan, change management, adoption measurement, and iteration cadence. Scaling is not just a technical exercise — it is an organizational one. The teams using the system need to understand it, trust it, and know what to do when it fails.
What happens in this phase
- Phased rollout plan with adoption milestones
- User training and change management support
- Adoption measurement framework and baseline tracking
- Feedback loop for continuous improvement
- Iteration cadence and backlog management
What Vendor-Agnostic Means in Practice
We work across all major foundation models and recommend based on your use case, your data environment, your security requirements, and your cost constraints — not based on partnership agreements or platform commitments. Here is how we think about each major model family.
Claude
Anthropic's model. Best-in-class for long-context document work, instruction following, and tasks requiring careful reasoning.
GPT-4o
OpenAI's flagship. Strong across a wide range of tasks; particularly well-suited for structured output generation and function calling.
Gemini
Google's model. Strong multimodal capabilities and competitive on cost for high-volume inference workloads.
Llama
Meta's open-source model. Best when you need on-premises deployment, data sovereignty, or fine-tuning on proprietary data.
Specialized models
Domain-specific models for healthcare, legal, finance, and code that outperform general models on narrow, high-stakes tasks.
The right answer depends on your specific use case. We run structured model evaluations against your actual data and workflow — not against benchmarks that do not reflect your environment. The recommendation we make is the one that performs best for you.
Request an Implementation Review
Tell us about your use case, your current state, and where you are stuck. We will give you an honest assessment of what it takes to move from where you are to production — and a clear path to get there.
Request an Implementation Review