Put AI agents to work across your company's processes.
Contextfy identifies where AI can drive results, integrates the data and systems it needs and puts agents and automations into operation, with control and measurable results.
- Use cases prioritized by business impact
- Integration with existing data and systems
- Controls and governance from the start
- Quality, cost, adoption and results tracked
- Source
- Scope
- Governance
- Quality
Why this matters now
The value of enterprise AI doesn’t come from more isolated pilots. It comes from trustworthy data, governed context and the ability to scale safely.
Data ready
Sources organized, mapped and ready to feed agents.
Governed context
Scope, permissions and traceability defined before you scale.
Focused on ROI
Use cases prioritized by real impact, risk and feasibility.
The problem
Many AI projects never reach operation because the process, data, integrations and controls were not prepared for real use.
Companies pile up documents, reports, spreadsheets and systems for years. When they put AI agents to work over that content, problems of context, governance and trust appear.
Scattered data
Information spread across documents, systems and spreadsheets, with no common context base.
Answers you can’t trust
AI helps in some cases, but it is hard to audit, trace and use in production safely.
Low governance
No control over sources, versions, access and policies for data use.
Unclear ROI
Promising pilots, but no objective metrics for business impact.
Business impact
What changes when agents run on trusted context
No magic promises. Concrete gains that show up once the base is production-ready.
Less rework
Consistent answers from approved sources cut manual correction and back-and-forth.
Faster internal sign-off
With traceable source, version and scope, IT, legal and security clear new agents with less friction.
Lower operational risk
Scope and permissions per agent prevent overly broad access and answers with no origin.
More agents in production
A governed, reusable base shortens the path from pilot to operation for every new case.
Faster onboarding
Organized, trusted knowledge ramps up new teams, partners and agents.
More value from what you have
Agents start making good use of the documents, systems and data the company already owns.
Agents in teams
AI agents are joining teams. Is your context ready?
Claude, ChatGPT, Copilot and other agents are starting to operate inside channels, workflows and corporate tools. The upside grows, and so does the risk: wrong sources, broad permissions, answers with no evidence and decisions that are hard to audit.
Shared context
The agent stops serving one person and starts serving a whole channel. A stale or out-of-scope source affects the entire team.
Scope per agent and channel
Each agent sees only the sources authorized for that team or channel, inheriting the access that already exists.
Evidence per answer
Every interaction leaves a trail of source, version and permission. You can prove where the answer came from.
Works with your stack, no lock-in
We work with ChatGPT, Claude, Copilot, Databricks and other environments. We build on the platforms you already have and apply the integrations, governance controls and context components each project needs.
Our thesis
Trust in AI starts with the quality of the context.
Contextfy works from diagnosis through implementation and operation. We start from the process and the expected outcome, define the architecture and controls, integrate data and systems and put the priority case into operation.
“It is not about building one more chatbot. It is about preparing the base so agents answer with a trusted source, the right context and proper controls.”
- Organized data
- Governed context
- Traceable sources
- Pilots with real value
Who it is for
Built for teams putting agents into production with control
A good fit for
- Mid-size and large companies with meaningful data and processes
- IT, data, AI, operations and compliance teams
- Teams that already tried AI and want to go from pilot to production
- Organizations with sensitive data, scattered knowledge and a need for traceability
Not the right fit for
- Anyone looking for a cheap, generic chatbot
- Simple automation, with no governance or traceability
- Teams without a minimum of organized knowledge sources
- Anyone who just wants prompt engineering or basic training
What Contextfy does not do
- Does not replace the runtime (Claude, ChatGPT, Copilot, etc.)
- Does not certify ISO 42001 nor act as an independent auditor
- Does not promise to eliminate 100% of hallucinations
- Does not connect agents to data without scope and permission
Real challenges
What stalls AI projects inside companies.
Many companies have already tried AI. The challenge now is to organize data, context and governance, turning experiments into useful, safe initiatives connected to the business.
We have data, but it’s scattered.
Documents, spreadsheets, systems and internal bases grow without a common context architecture. Before building agents, you need to know which sources are trustworthy.
The pilot worked, but we don’t know how to scale.
Many AI initiatives get stuck in demos. Contextfy helps define governance, quality criteria and a roadmap to move forward safely.
Leadership wants AI, but operations aren’t ready.
The push for adoption has to come with data maturity, clear use cases and alignment across technology, business and risk.
We don’t want one more tool without governance.
Contextfy prepares the foundation so tools, models and agents operate on trustworthy context, with sources, scope and traceability.
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.
What we deliver
What your company gets working with Contextfy.
More than a generic AI recommendation, we deliver clarity on data, risks, opportunities and next steps to build trustworthy agents.
Readiness diagnostic
Assessment of maturity across strategy, data, governance, technology, use cases and risk.
Source & context map
Identification of the sources, documents, systems and knowledge bases that can back agents.
Use-case matrix
Prioritization of use cases by impact, feasibility, risk and context availability.
Architecture blueprint
Design of the architecture, integrations, rules of use and controls to put the initiative into operation.
Controlled pilot
Validation of a real case with defined scope, traceable sources and quality metrics.
Scale roadmap
A plan of next steps to evolve from pilot to continuous operation.
Still mapping where to start?
Still figuring out where your company can move forward with AI? Take the free diagnostic.
For teams still shaping the initiative. Answer a few questions and get an initial read on use cases, data, governance and next steps.
What the diagnostic assesses
- Strategy
- Data and documents
- Governance
- Technology
- Use cases
- Risk and compliance
Under 5 minutes · No commitment · Instant result
Current level
Preparation
Solutions
Where Contextfy creates value.
We apply the same approach to processes where data, documents and internal knowledge make a difference. Always with organized context and clear rules of use.
Internal support
Assistants for policies, processes, internal documents and employee support.
Sales & pre-sales
Agents for proposals, sales material, product knowledge and FAQs.
Operations & processes
Smart automation of flows, document analysis and operational support.
Governance & compliance
Control of sources, scope, audit, traceability and security in AI use.
Data analysis & reporting
Assistants to interpret reports, indicators, documents and corporate bases.
Knowledge & training
Onboarding and lookup of playbooks, policies and procedures in natural language.
Pillars
The pillars of governed context
Authority guides on the topics behind trustworthy AI agents in production.
AI agent governance
Control what each agent uses, who accesses it and how to prove it.
AI audit
Per-interaction evidence: source, version, scope and permission.
Governed RAG
Retrieval with approved source, version, scope and trail.
Enterprise MCP
Serve governed context to any runtime over MCP.
Context engineering
The discipline of preparing the context layer before the agent.
ISO/IEC 42001
AI management system: the governance that structures conformance.
Agent observability
Measure agents’ value, quality and risk in production.
ContextOps
The continuous operation of context, from pilot to scale.
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.
Technology and accelerators
Methodology and accelerators to shorten the path to operation.
Contextfy combines proprietary components and market solutions to speed up integration, evaluation, governance and evidence generation. The technology supports the project; engagements start from the problem and the expected outcome.
- Source ingestion
- Curation & versioning
- Sourced answers
- Quality Score
- API & MCP Server
- Widget & channels
- Source
- Scope
- Governance
- Quality
Blog
Content to move forward with agents without skipping steps.
Strategy, data, governance and use cases: what decision-makers need to know before scaling AI.
Ensuring LGPD Compliance in AI Solutions
Explore best practices and technological solutions to ensure your AI implementations comply with Brazil's LGPD regulations.
AI for customer service: what changes when the answer has context
Applying AI to support is more than a chatbot on your site. See what separates an automation that frustrates from an agent that resolves, with source, scope and clear rules.
Document chatbot: how to build a reliable base for corporate use
A document chatbot your company actually uses depends on the base: source of truth, scope, permissions, source citation, and measurement. The step-by-step.
Frequently asked questions
Straight answers before you talk to us.
Consultancy, diagnostic, implementation, compatibility and security. No fluff.
Is Contextfy a consultancy or a platform?
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, not from buying a platform. Our technology components cut the time to diagnose, integrate and produce evidence, without forcing your company to switch stack or vendor.
Is the free diagnostic really free?
Yes. The initial diagnostic is free and helps identify your company’s readiness for AI agents. It shows strengths, risks and recommended next steps.
What happens after the diagnostic?
After the diagnostic, we point to the path that best fits your company’s moment: a deeper AI Diagnostic and Implementation Plan, the Agent and Automation Deployment for your priority process, or Continuous AI Governance and Operations. The recommendation depends on urgency, sponsorship and how clear the problem you want to solve is.
Does Contextfy deploy the agent, or just prepare the data?
Contextfy deploys. We can lead the deployment end to end, take on specific components or work in co-delivery with your team and the vendors your company already uses. Before going live, we align sources, rules, risks, permissions and quality criteria, so the agent reaches production safely and with measurable results.
Do you replace tools like ChatGPT, Claude, Gemini, Copilot or automation platforms?
No. We work with those tools: we deploy and integrate agents and automations into the stack you already use, applying the integrations, context controls and governance each project needs. The choice of execution platform stays yours.
Do I need a structured database to start?
No. Many companies start with documents, spreadsheets, internal pages, legacy systems and scattered knowledge. The first goal is exactly to understand what exists, what is trustworthy and what needs organizing.
How do you reduce wrong answers or hallucinations?
We reduce risk by working with defined sources, controlled scope, sourced answers, quality criteria, evaluation and governance. No solution eliminates 100% of errors, but a good context layer reduces the risk significantly.
Do you work with sensitive data?
We can support assessment and architecture design considering sensitive data, privacy regulations, permissions, retention and audit. Access and storage depend on each company’s scenario.
How long does an initial project take?
It depends on scope, the sources involved and how clear the use case is. We usually start with a short diagnostic to understand the scenario, identify risks and prioritize opportunities. From there, the work evolves into deploying the priority case into operation. More important than duration is the fixed scope: each stage has clear objectives, deliverables and criteria to move forward.
How are projects contracted?
Projects are defined by scope and deliverables, not by loose consulting hours. After the initial diagnostic, we recommend the format that best fits the company’s moment: AI Diagnostic and Implementation Plan, Agent and Automation Deployment or Continuous AI Governance and Operations. Each stage has clear objectives, agreed deliverables and criteria to decide the next step.
What’s the best first step?
The best first step is the free diagnostic. It helps you understand which stage your company is in and what to prioritize. From there, it’s easier to define the next move without starting with the wrong tool or a poorly structured pilot.
Trust and credibility
Built for business decisions, not just AI demos.
- Data security
- Traceability
- LGPD / GDPR
- Secure infrastructure
- Governance
- ROI focus
Start with the diagnostic
Is your company ready for AI agents?
Start with a free diagnostic and understand which data, processes and risks you need to address before scaling AI in your business.