What Contextfy's AI consulting is
It is the consulting that takes AI initiatives from pilot to operation, instead of piling up proofs of concept that impress in the demo and stall in production. It is not generic AI strategy and it is not a POC factory. It is the work between the early excitement and the agent your company trusts to serve a customer or back a decision.
We start from the problem and the process. We prioritize the use case, define the architecture, integrations and controls, and lead the deployment of the selected case. We can execute directly, lead the deployment end to end, or work in co-delivery with your team and the vendors already engaged.
Context engineering, evaluations and evidence ensure the agent uses approved sources, clear permissions and a reliable trail. But we do not sell an isolated technical component: what your company hires is a real process running on AI, with quality, control and outcome indicators. The choice of execution platform stays yours.
We run on what we deploy for you
Contextfy operates with supervised agents in its own day-to-day, in processes such as market intelligence and content production and review. They follow the same principles we apply in a project: defined sources, human review at critical points, traceable executions and continuous quality evaluation.
That changes the conversation. Instead of slides, we show a real process running on AI and how the same approach applies to your scenario. It is the most direct way to see, before you hire, how we define scope, context, controls and how the agent behaves when information is missing.
Who it is for (and when)
This consulting is built for mid-to-large companies that have already invested in AI and now need to turn tests into operation. If you recognize yourself in any of the situations below, the right place to start is the assessment.
The pilot worked, but nobody clears it for production
The demo convinced everyone, except IT, legal, and security will not approve real use, because there is no way to audit the answer or to guarantee the agent uses only what it should.
The knowledge is scattered
Policies, contracts, proposals, and procedures live fragmented across SharePoint, Drive, CRM, ERP, and tickets. Each agent pulls from a different place and nobody knows which version is the official one.
Scaling without control feels risky
There is appetite to build dozens of agents, but leadership holds back because each new one seems to widen the exposure instead of the return. What is missing is the criteria, ownership and controls to scale with confidence.
Answers nobody can defend
The agent responds, but with no approved origin. When someone questions it, there is no way to show which document it came from or who had permission to see that.
What stalls AI without this preparation
The bottleneck is rarely the model. It is the absence of approved sources, permissions, tests, owners and evidence to operate safely. Without that, every AI initiative turns into expensive exposure, and the bill arrives the moment you try to scale.
- A POC that works but does not scale. What shines in a controlled demo breaks in the operation for lack of scope, versioning, and an audit trail. The effort turns into rework and leadership's confidence drains away.
- Shadow AI. Teams spin up agents on their own, wired to data with no curation, no owner, and no source criteria. Nobody knows how many exist or what they reach.
- Answers without an approved source. When the agent answers from whatever material it happens to find, the company takes on the risk of information that is wrong, expired, or that should never have circulated.
- Permissions that are far too broad. Agents inherit wide access and can hand sensitive data to people who should not see it. Poorly defined authority is one of the biggest sources of exposure.
- Internal sign-off stuck. With no trail showing the origin, version, and reach of each answer, risk and compliance committees will not clear it. The project stays in pilot forever.
How we deliver: from diagnosis to continuous operation
We deliver from diagnosis to continuous operation, and we start small: one area, one priority case, measurement, and only then scale. No disruption to the current operation.
First, the diagnosis maps the process, the sources, the permissions, and the gaps, and defines the architecture and controls. Then we deploy the agent or automation into the real process, with approved sources, scope per agent, and evidence traceable to the document. Finally, continuous operation keeps quality, cost, and risk under control as new cases come online. All of it stays independent of the model and the execution platform, because that choice stays yours.
Fontes
Drive, SharePoint, ERP, CRM, PDFs, APIs
Contextfy · Context Engine
Organiza · versiona · governa · observa o contexto
Runtimes
via MCP · API · conectores · pipelines
What you get
The consulting delivers concrete artifacts, ready for leadership to decide on and for the team to execute. No generic slides. Each item tackles a specific obstacle between the pilot and production.
Source map
An inventory of the knowledge that feeds (or should feed) your agents, with the origin, owner, and state of each base.
Permission matrix
The who-sees-what per agent and per team, inheriting the access rules your company already has. It puts an end to authority that is too broad.
Gap inventory
The questions your agents cannot answer and what is missing to fix that: bases that are absent, expired, or in conflict. What to repair before you scale.
Context quality score
An objective read on the health of the knowledge base: coverage, freshness, consistency, and blind spots. Comparable over time.
Production roadmap
An implementation plan prioritized by risk and by value, from the first agent in production to scale. No vague promises.
An audit-ready base
Knowledge organized so every answer carries a traceable origin, version, and reach. The evidence compliance and audit ask for.
The business gain
Here control works for speed, not against it. With approved sources, defined authority, and a trail in place, the teams that usually stall projects start approving with less friction. The practical effect is more AI in production and less investment parked in pilots.
More agents leaving the pilot
The main return is reducing the risk of investing in AI initiatives that never reach the operation. With the base prepared, new cases move from the demo into production faster.
Less rework and manual review
When the agent answers from an approved origin, the need to check every output by hand drops. The team reviews the exceptions, not everything.
Faster internal sign-off
Every answer carries its origin and evidence. Risk, legal, and security committees clear new agents with more confidence and in less time.
Lower risk as you scale
Correct authority and traceability mean less exposure with each agent that comes online. Growth stops widening the blind spot.
Value in weeks
Starting with the diagnosis and one area, the first results show up in weeks, not across a year-long project. You measure before you scale.
How to start
The assessment is the front door, and it does not disrupt your current operation. Instead of a long transformation project, we start small: one business area and two to four relevant sources. We map what exists, where the gaps are, and what is missing to put agents into production with confidence.
What you walk away with is concrete: the source map, the permission matrix, the gap inventory, a context quality score, and a roadmap prioritized by risk. Enough material for leadership to decide on facts, not on a promise.
This is how you complete the arc from pilot to production without betting everything at once. You measure the gain in the first area, earn the trust of the control teams, and only then scale to the rest of the agents. Start with the assessment.
Frequently asked questions
What does Contextfy's AI consulting do?
It takes AI initiatives from pilot to operation. We prioritize the use case, define the architecture, integrations, and controls, integrate data and systems, and lead the deployment of the agent or automation into the real process, directly or in co-delivery with your team and the vendors already engaged. Context engineering, approved sources, scope, and evidence hold up the solution, but what you hire is a process running with quality, control, and outcome indicators.
How is this different from a traditional AI strategy consultancy?
Generic strategy consulting delivers vision, slides, and high-level roadmaps. This consulting delivers the operation that is missing between strategy and production: the agent or automation deployed into the process, with proportional controls, clear permissions, and answers with a traceable origin. We are also not a proof-of-concept factory. The goal is to put agents into operation, not to stack up pilots that never scale.
Do you deploy the solution, or just prepare the data and context?
We can lead the diagnosis, the architecture, and the deployment end to end, take on specific components, or work in co-delivery with your team and the vendor already serving the company. The model depends on the case, the existing stack, and the skills available, without forcing a change of platform or partner. Context engineering, approved sources, scope, permissions, and evidence hold up the solution, but what you hire is a real process in operation.
Does the consulting work with any model or agent platform?
Yes. The context layer is independent of the model and the execution platform. It is compatible with architectures based on API and MCP, so the agents you already use or plan to adopt consume the same governed context, keeping origin, authority, and evidence consistent across tools.
Does Contextfy replace my agent platform?
No. Contextfy is a consultancy specialized in AI implementation and governance, backed by its own methodology and accelerators. Engagements start from the problem and the project. Our technology components cut time, add consistency, and produce evidence, without forcing your company to replace its stack or buy a new platform. You keep your agent platform.
How long until we see value, and how do we start?
We start with the assessment, with no disruption to the current operation, on one area and two to four sources. The first results show up in weeks, as a source map, a permission matrix, gaps, a score, and a roadmap. You measure the gain in the first area before scaling to the rest of the agents.
Keep exploring
No disruption: it starts with one area and two to four sources.
Start with the assessment