Agentic AI, built on your data
Agents are only as smart as the data they reach.
Chat portals are everywhere now. The companies pulling ahead have agents that work across their real systems and do real work. We’ve spent 25 years in data. Now we build the agents that run on it.
Get an Agent Readiness AssessmentSee our agents
TODO: Assessment not confirmed; this button goes with it
- 25+
- years in data and analytics
- 100+
- data sources connected to AI
- 4,000+
- enterprise users served
- 160K+
- AI interactions delivered
The problem
Everyone has AI. Few have results.
Most companies bought a chat tool and ran a few pilots. Then progress stalled. The research is consistent about why: they added AI to individual tasks, on top of data it couldn’t reach.
The fix is to redesign one workflow end to end, connect it to the data that drives it, and let an agent run it.
- 80%+
of companies report no bottom-line impact from their AI investments yet.
- 23%
are scaling an AI agent anywhere in the business. No more than 10% are doing it in any single function.
- 3×
Companies getting real value are about three times as likely to have redesigned whole workflows, not just tasks.
Where AI is heading
Portals answer questions. Agents do the work.
Then:Dashboards
Data you look at
Reports told you what happened. People still had to find the insight and act on it.
Recently:Chat portals
AI you ask
Secure chat gave every employee an assistant. Useful, now standard, and it stops at an answer.
Now:Agents
AI that acts
Agents pull from your systems, reason over the results and finish multi-step work. People set the goals and make the calls that matter.
We have always done data
The big firms talk about agents. We start with what’s underneath them.
Most agent projects fail at the data: it’s scattered, ungoverned, or out of reach. We’ve connected AI to more than a hundred different sources, so we know what your systems will throw at an agent before it goes live.
100+Data sources connected to AI
Connect
MCP servers and integrations give agents secure, governed access to your databases, applications and APIs. Your data stays where it is.
Ground
Retrieval systems built on your own content, so agents cite your documents and your numbers.
Govern
Permissions, audit trails and policies carry through from the data layer to every action an agent takes.
Secure
Multi-cloud and behind-your-firewall deployments across three cloud platforms. Nothing leaves your control.
Agentic AI
We’ve built more agents than we can count.
Each one is designed around a real workflow, wired into the systems that workflow touches, and measured on the hours it gives back.
Human in the loop
The agent does the work. A person approves it.
Right for drafts, decisions and anything customer-facing. The agent prepares; your team signs off.
Human above the loop
The agent runs the process. A person handles exceptions.
Right for high-volume, rules-based work. Your team sets the rules and steps in only when the agent flags something.
TODO: Five example agents: typical patterns, not client work. Approve or cut
Research
Answers that took a week, in minutes
Searches internal documents, databases and approved outside sources, then writes a cited brief.
- Reads
- SharePoint, warehouse, web
- Does
- search, compare, summarize
- Delivers
- a cited brief
Reporting
The monthly report, drafted for you
Pulls numbers from source systems, checks them, explains the variances and writes the narrative.
- Reads
- ERP, CRM, finance data
- Does
- query, reconcile, explain
- Delivers
- a draft for review
Operations
Routine work, handled end to end
Watches for triggers, takes the next step in your systems and escalates exceptions to a person.
- Reads
- tickets, orders, inventory
- Does
- detect, act, update
- Delivers
- exceptions only
Customer
Service grounded in your records
Responds using the customer’s real account history, policies and order status, inside your guardrails.
- Reads
- CRM, knowledge base
- Does
- look up, respond, log
- Delivers
- complex cases to staff
AI Think Tank
Agents that debate before they decide
Several agents take different positions on a problem, challenge each other and converge on a stronger recommendation.
- Reads
- your brief and data
- Does
- propose, critique, refine
- Delivers
- a recommendation
Example agent patterns. Client-specific builds available on request.
Vetted helped our team rethink how we work. We went from skeptical to engaged within weeks.
Services
We don’t just advise. We build.
Build
Custom AI Agent Development
Agents for your workflows and industry, connected to your systems.
MCP Integration & Build-Out
Connect AI to your databases, applications and APIs through Model Context Protocol.
RAG & Data Readiness
Get your data ready for agents and ground them in your own content.
Run
Secure AI Portal Deployment
Enterprise AI platforms deployed behind your firewall.
Agent OperationsNew
Monitor, tune and govern your agents after launch.
TODO: Agent Operations and the New label not confirmed
Governance & Compliance
Policies, permissions and audit for responsible AI.
Advise
AI Strategy & Roadmapping
High-impact use cases, business cases and a roadmap.
AI Advisory Program
Fractional AI leadership: strategy, vendor evaluation, governance.
Engineering Pods
AI engineers embedded with your team, with knowledge transfer built in.
Vibe Coding Training
Teach your people to build AI apps with natural language.
Our approach
One workflow. In production in about three months.
Large firms sell multi-year transformations. We start with one workflow that matters, prove it in production, then scale what works.
TODO: Week-by-week timeline not confirmed; we only claim “3 months to production”
Weeks 1–2
Discover
Pick the workflow and map the data sources it depends on.
Weeks 3–4
Design
Decide what the agent reads, what it may do, and where people stay in the loop.
Weeks 5–12
Deploy
Connect the data, build the agent and put it into production in your environment.
Then
Scale
Measure the results and extend to the next workflow and the next team.
TODO: Two-week Agent Readiness Assessment not confirmed. Approve or cut
Start here
Agent Readiness Assessment
A two-week, fixed-scope review with our senior team. You come away knowing which workflow to automate first and what it will take.
- Workflow shortlist. The three processes where an agent would return the most time.
- Data readiness check. Which sources the agent needs, and whether they’re ready.
- Agent design. What it reads, what it does, and where people approve.
- Business case. Expected hours saved, cost and timeline to production.
- Governance plan. Permissions, audit and security for your environment.
Results
What happens when AI is built on the right data.
- 85%
- user adoption rate
- 40%
- reduction in manual research
- 3 mo
- to production
- 24/7
- global availability
Insights
Thinking from the build floor.
TODO: Three “coming soon” articles have no text yet. Approve or cut
Why your AI pilot stalled, and why it was the data
The patterns we see when agents meet real enterprise systems.
In the loop or above it? Choosing how much an agent decides
A practical guide to human oversight for each kind of workflow.
The chat portal is a commodity. Now what?
What to do after every employee already has an AI assistant.
Why Vetted
Data people who build agents.
Data first
25 years of data and analytics. We fix the foundation before agents go on top.
Practitioners
Our methods come from systems we’ve built and run.
Your firewall, your data
Deployed in your environment. Nothing leaves your control.
Enterprise depth, mid-market pricing
The work of a large firm, scoped for growing businesses.
Senior people throughout
The people you meet are the people who build.
U.S.-based team
All consultants and engineers are in the U.S., in your time zones.
Start a conversation
Which workflow should your first agent take on?
Bring us one process and the systems behind it. We’ll show you what an agent could do with it.
Schedule a conversationAbout the assessment
TODO: Scheduling link or email for this button
TODO: “About the assessment” goes with the unconfirmed assessment