Contextfy methodology
From diagnosis to operation in short stages, with clear criteria to move forward.
In a few weeks, your company moves from scattered ideas to a prioritized case, an implementation plan and clear criteria to move forward. Each stage closes with concrete deliverables and a go/no-go decision, from diagnosis to operation.
- Focus on business cases, not isolated experiments
- Use case, process and controls before the tool
- Traceability and audit from the start
- Responsible scalability, with ROI metrics
Methodology
What happens at each stage
Five stages with clear deliverables and success criteria, from diagnostic to operation. Each one lowers risk and shows concrete progress before you scale.
- 01
Process and opportunity diagnosis
We understand the process, the data, the risks and the expected outcome before proposing any solution.
- 02
Use-case prioritization
We choose where AI creates the most value, weighing impact, risk and deployment effort.
- 03
Architecture, integrations and controls
We define the components, data, systems, permissions and criteria needed to put the initiative into operation.
- 04
Deployment and validation
We put the agent or automation into controlled operation, with before-and-after metrics and traceable sources.
- 05
Operation and evolution
We track quality, cost, risk and adoption, and plan the next steps safely and with clear returns.
Offers
Three ways to engage, from diagnosis to continuous operation.
AI Diagnostic and Implementation Plan
Typical duration, depending on scope: 1 to 2 weeks
Who it’s for
For companies with an opportunity, process, project or pilot that needs direction.
Deliverables
- Process and use-case analysis
- Prioritized opportunities and operational baseline
- Required data, sources and integrations
- Risks, controls and recommended architecture
- Business case and 30/60/90 implementation plan
Agent and Automation Deployment
Typical duration, depending on scope: 4 to 8 weeks
Who it’s for
For companies with a priority case, a sponsor and a defined process.
Deliverables
- Workflow and architecture design
- Integration with data and systems
- Agent or automation in controlled operation
- Sources, permissions and quality and refusal criteria
- Before-and-after metrics and evolution plan
Continuous AI Governance and Operations
Typical duration, depending on scope: Recurring monthly service · ContextOps
Who it’s for
For companies with agents already deployed or a growing portfolio of initiatives.
Deliverables
- Monitoring of quality, cost and adoption
- Evaluations, source coverage and evidence
- Access, incident and refusal control
- Executive report and outcome indicators
- Agent evolution backlog
Deliverables by phase
What you get at each phase
Every phase closes with concrete material that leadership can read and the technical team can act on. No generic report: an inventory, a permissions map, the gaps, and a prioritized path to move agents from pilot into production.
Agent and source inventory
We map every agent in operation, what it is for, and the sources it consumes, separating what is already OFFICIAL from what is still DRAFT. It gives your company a single view of what the AI actually relies on today.
Permissions matrix
A who-sees-what grid by collection: which agent reaches which knowledge base, under what scope, through which API keys. It lets you close overly broad access before an agent goes live.
Gap map
We list where approved sources are missing, where content is stale, and where the agent refuses on insufficient_context. It lets you prioritize curation by what blocks trustworthy answers the most.
Context Quality Score
An objective read on the coverage, freshness, consistency, and traceability of the base that feeds your agents. It works as a baseline to decide what is fit for production and what still needs work.
Risk-prioritized roadmap
A work sequence ordered by exposure and impact, not by backlog size. It lets leadership see what unlocks the most value at the least risk in each sprint.
Executive report
A synthesis for the C-suite and steering committee covering the state of the base, risks, active controls, and the evidence that supports the case to move forward. Backed by the Evidence Log with traceId, which records the question, context used, sources, and answer for each interaction.
How we run it
How we govern and measure success
Governance here is not a brake: it is the mechanism that takes an agent out of the pilot and into trustworthy operation. We work by clear criteria, in a small scope, with evidence as proof that scaling is worth it.
Criteria-based go/no-go
Moving from pilot to production has criteria set before we start: approved sources, a closed scope, safe refusal when context is missing, and a clean Evidence Log. Without those met, the agent is not promoted.
Start without disruption
We begin with one operational area and two to four sources in the first sprint, attaching to what already exists through API or MCP. You measure before scaling, with no big-bang project that halts the operation.
An owner per agent
Each agent has a purpose, a scope, and an internal owner for curating the sources it consumes. This keeps shadow AI out and keeps accountability clear over what the AI uses.
Continuous measurement (ContextOps)
We track refusals, gaps, and most-used sources in live operation, not just in the demo. The base improves from what real usage reveals, keeping context governed as it grows.
Evidence as proof of value
Every answer leaves a trace with a traceId: which sources backed it, what scope was applied, and what the outcome was. When audit, legal, or the board asks why the agent answered that way, there is a record, not improvisation.
Frequently asked questions
- How does the Contextfy methodology work in practice?
- The engagement runs in four stages: a diagnostic (mapping sources, agents, risks and gaps), a context blueprint (architecture, source policy and permissions model), a governed pilot (a real agent with a closed scope, approved sources and an evidence trail) and ContextOps (ongoing operation that measures and improves the base). Each stage starts small, in a priority area, and closes with a criteria-based go/no-go before moving on.
- What is the timeline and how soon do I see value?
- The diagnostic delivers results in 1 to 2 weeks, the context blueprint in 3 to 4 weeks and the assisted pilot with real data in 6 to 8 weeks, depending on scope. Value shows up early: the diagnostic alone gives you an inventory, a gap map and a prioritized path, with no need to wait months for a large project.
- What do I get at each phase?
- You get concrete artifacts that leadership can read and the technical team can act on: an agent and source inventory, a permissions matrix (who sees what), a gap map, a read on context quality and a risk-prioritized roadmap. In the pilot and ongoing operation, each answer leaves a trace with a traceId — question, context used, sources and outcome — that feeds the executive report.
- How is this different from a traditional AI consultancy?
- Traditional consulting tends to deliver slides and recommendations; here the outcome is governed, operable context — approved sources, per-agent scope, safe refusal when the base is missing and an evidence trail. We do not compete with the runtime you use (Claude, OpenAI Agents, Copilot Studio): we govern the context those agents consume, and the deliverable lives in the operation, not just in a report.
- Do I need to halt operations to get started?
- No. We start with one area and two to four sources in the first sprint, attaching to what already exists through API or MCP, with no big bang. You measure in a small scope before scaling, so the team keeps working while the pilot runs in parallel.
- How do I start and what happens in the first step?
- The entry point is the executive AI and context diagnostic. In 1 to 2 weeks, we map your sources, agents and risks, identify the priority use case and deliver a report with gaps and next steps. From there you decide, with clear criteria, whether to move on to the blueprint and the pilot.
Start with the diagnostic
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