Proprietary components to accelerate AI implementation and governance.
Contextfy uses proprietary components and market solutions to accelerate diagnosis, integration, evaluation, governance and evidence generation. These capabilities are applied inside projects and evolve from the needs found in real deployments. The Context Engine organizes, versions and serves context to any agent via MCP, API and connectors.
Consulting maps and organizes the context; the platform keeps it governed, measured and continuously available (ContextOps), for any runtime.
- Source
- Scope
- Governance
- Quality
What the Context Engine does
The technical capability behind the promise: from raw knowledge to the governed context agents consume.
Source connection & approval
Connect documents, bases, ERPs and drives. Every source with approval status and version.
Context Engine
Ingestion, normalization and versioning of knowledge into agent-ready collections.
Governance & scope
Approved sources, permissions per agent and an audit log of every query.
Observability & quality score
Context Quality Score, knowledge gaps and unanswered questions to improve the base.
API, MCP & connectors
Serve the same governed context to any runtime, with no lock-in.
ContextOps
Continuous context operation: monitoring, evidence and improvement, from pilot to scale.
The methodology is built to evolve into a continuous operational context layer (ContextOps).
Book a callNo operational disruption
It plugs into what you already have. No big bang.
Reuse the investments you have already made
Instead of rebuilding processes or migrating systems, the context layer connects existing documents, systems and sources and delivers value from the very first use case.
Get results without waiting months for a project
One priority case and one approved source are enough to validate value before expanding to other areas.
Scale on evidence, not perception
Before rolling an agent out to the whole company, you know which sources it covers, where the gaps are and how confident the answers are.
Your base stays valuable even as the technology changes
The same context structure serves new agents, models and runtimes, reducing dependence on specific vendors.
Works with your stack
It fits the AI tools and sources you already use
The same governed-context base serves different models, agents and sources, through an API, MCP and connectors. No lock-in.
Models
- OpenAI
- Claude
- Gemini
- Azure OpenAI
Agents & frameworks
Data
- Databricks
- Snowflake
- PostgreSQL
- S3
Knowledge
- Google Drive
- SharePoint
- Confluence
- PDFs
Integration
- MCP
- API REST
- Webhook
- Python
- Node.js
- TypeScript
Trademarks of their respective owners. Contextfy is an independent governed-context layer, compatible with API- and MCP-based architectures.
Ready for agents in teams
Ready for agents in channels and teams
When agents start operating in Slack, Teams, GitHub, CRM, data warehouses and workflows, context stops being individual. The platform keeps sources, scope, versions and evidence consistent across different agents, channels and runtimes, so an answer given in a shared channel carries the same trusted origin as always.
See AI agents in teamsThe platform in practice
The assets you operate with Contextfy
We do not just organize context: you start operating it. Registry, governance, quality, coverage and trail in one place.
Context Registry
Catalog of context collections: sources, versions, owners and approval status in a single inventory.
Source Governance
Who-sees-what per agent and team, with the approved source kept separate from the raw one.
Quality Score
Context readiness in one indicator: coverage, freshness, consistency and gaps.
Coverage
What agents already cover with confidence and where sources are missing to answer.
Audit Trail
Per-interaction trail: sources, version, scope and permission. Evidence ready for audit.
Agent Metrics
Agents in production per area, adoption, answers with evidence and cost per query.
How Contextfy is built
A layered architecture
Contextfy splits the context lifecycle into independent layers: ingest and curate sources, govern what becomes official, serve any agent, and observe every answer. You can swap the runtime without rebuilding a single one of these layers.
Ingestion and curation
The Context Engine takes in documents, knowledge bases and scattered material, organizing everything into collections and context packs. Raw sources never reach an agent without curation first.
Source governance
An Approval Queue separates draft from official: content only becomes an approved source (DRAFT to OFFICIAL) once scope, version and owner are set. This is the layer that decides what an agent is allowed to consume.
Serving any agent
Governed context is delivered over REST (/v1/search, /v1/ask), over MCP and through connectors. The same approved corpus feeds different agents with no rebuild per runtime.
Scope applied at serving
Every call honors the collection scope and the intersection with API keys (allowedCollectionIds). An agent only sees what its access permits, decided at serving time rather than configured per app.
Observability and evidence
The Evidence Log records the question, context used, sources, outcome and a traceId for each interaction. Analytics surfaces gaps, refusals and most-used sources so you can improve the base continuously.
Context quality
On top of that trail, the architecture is designed for a Context Quality Score that synthesizes coverage, freshness, consistency and traceability. It is how a company gauges whether a base is fit to feed agents before scaling.
Data control
Security and data control
Who-sees-what is not a parallel setup: Contextfy applies the access your company already has and isolates each area. With no approved source, the agent refuses rather than inventing, and every query leaves a trail.
Scope by collection and workspace
Access is governed by tenant, organization, workspace and collection scope. Each agent gets an explicit boundary over which sources it can use, instead of overly broad access to the whole corpus.
Inherited permissions
Serving honors permissions through the scope intersection on API keys (allowedCollectionIds). Access rules follow whoever is asking, with no separate permission model built just for the AI.
Isolation between areas
Collections and workspaces keep legal, sales and support apart. One area's context does not leak into another area's agent, even when both run on the same runtime.
No source, no answer
When approved material is missing, the agent refuses with insufficient_context instead of improvising. An honest refusal is part of the control, not a product failure.
Per-interaction trail
Each query produces an Evidence Log entry with sources, score, outcome and traceId. If security or audit asks where an answer came from, there is proof tied to the interaction.
Sensitive data and LGPD
The DRAFT to OFFICIAL cycle and collection scope help keep sensitive content out of the wrong hands. The controls are designed to support data-protection requirements around access and purpose.
No lock-in
Standards and interoperability
Context lives outside the runtime. Contextfy speaks REST, MCP and connectors, so the same governed corpus serves Claude, OpenAI, Copilot, LangGraph or CrewAI without rewriting the context layer on every switch.
REST API
Endpoints like /v1/search and /v1/ask deliver context and sourced answers over HTTP. Any application or pipeline can consume the approved corpus without coupling to a specific runtime.
Governed MCP
The MCP server exposes tools such as search_context, ask_with_sources, report_feedback and report_unanswered. The agent receives context through the Model Context Protocol already inside its scope and evidence trail.
Connectors and pipelines
The architecture is built to integrate sources via connectors, jobs and exports. You start with one priority source and expand without rebuilding the base for each new integration.
Runtime independence
Compatible with API and MCP based architectures, governed context feeds different runtimes in parallel. Switching agents becomes a product decision, not a data migration.
No per-agent rebuild
Instead of each runtime assembling its own context source, all of them consume the same approved, versioned corpus. One curation effort, many agents, consistent control.
The context stays yours
Because corporate knowledge lives in the context layer rather than locked inside the runtime, the company keeps its reversibility. You adopt a new agent without being held hostage to a single vendor.
Frequently asked questions
- What is the Contextfy platform?
- It is the governed-context infrastructure that feeds AI agents in production. The platform prepares, versions, governs and audits corporate knowledge (documents, data, policies, CRM, ERP) and delivers it in a controlled way for agents to consume. Contextfy governs the context; the execution runtime is your choice.
- How does the platform integrate with my current stack?
- Through REST API, MCP (Model Context Protocol) and connectors, without replacing what you already use. The architecture is designed to attach to your sources (SharePoint, Google Drive, databases, internal APIs) and to serve context to agent frameworks via API, MCP or assisted pipelines. It is a layer that fits into the existing operation, not a platform that forces migration.
- How does the platform handle security and sensitive data under LGPD?
- Access is controlled by scope and per-agent permissions: each agent only sees the approved sources within its remit. The platform separates raw sources from approved sources, records who-sees-what and keeps a trail of every interaction, supporting sensitive-data handling in line with LGPD. The goal is to reduce exposure and provide traceability, not to replace the company's privacy program.
- Which agent runtime does the platform work with?
- Whichever you prefer. Contextfy is runtime-agnostic and designed to complement agent frameworks and platforms based on API or MCP, such as OpenAI Agents, Claude, Copilot Studio, LangGraph, CrewAI and in-house orchestrators. The platform does not compete with these runtimes; it governs the context they consume.
- What exists today and what is capability still being built?
- Today Contextfy delivers context preparation and governance through diagnosis, source organization and controlled pilots, with delivery via API and MCP. Capabilities such as Context Registry and Evidence Vault describe the platform's direction for registering sources, versions and evidence, and evolve alongside each deployment. We do not sell these as off-the-shelf products; delivery starts with your priority case and scales from there.
- How do I start using the platform, and is there a lock-in risk?
- You start with a diagnosis that maps sources, risks and the priority case, move to a controlled pilot and scale within weeks, with no big bang. Because context is served over open standards (REST API and MCP) and your sources stay where they are, there is no lock-in: you switch runtimes without rebuilding the context base. The layer attaches to what already exists and measures results before expanding.
Start with the diagnostic
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