AI Advisors
AI that understands your business before it advises it.
Connect your CRM, ERP, documents and spreadsheets to a private AI Advisor built around your operation. Ask questions, find out what changed, surface problems, and get a recommendation you can check against the data behind it.
- Your data stays yours — no training on client data.
- Starts read-only.
- A human makes the call.
What is an AI Advisor?
An AI Advisor is a custom AI system connected to a company’s own data — CRM, ERP, accounting, documents and internal tools. It reads that information in business context, answers plain-language questions, spots patterns and problems, and recommends what to do next. It does not act on its own: a person reviews the recommendation and makes the decision. It is decision support built around one specific business, not a generic chatbot.
Ask your business a question.
This is what an executive actually types — not a keyword search, a real question about what is happening.
Your business already has the answers.
A CRM knows the pipeline. An ERP knows production. Accounting knows margin. Email and spreadsheets know the rest. An AI Advisor becomes useful once it can read all of that together instead of one tool at a time.
- Salesforce
- HubSpot
- QuickBooks
- Stripe
- Google Workspace
- Microsoft 365
- Slack
- ERP systems
- Database
- Spreadsheets
- Internal APIs
If it exposes usable data through an API, database, export or supported connector, it can often become part of the Advisor’s context. We are not claiming to integrate with every system — only the ones that actually expose data we can work with.
One architecture, adapted to what your team needs to know.
The same underlying system, scoped to different questions and different data.
Executive AI Advisor
Answers cross-functional questions and surfaces priorities for leadership.
e.g. “What changed across the business this week?”
Operations AI Advisor
Analyzes delays, throughput, capacity and workflow problems.
e.g. “What is slowing us down right now?”
Sales AI Advisor
Analyzes pipeline, opportunities, accounts and follow-ups.
e.g. “Which deals need attention before Friday?”
Financial AI Advisor
Analyzes margins, expenses and cash-flow patterns from connected, authorized data.
e.g. “Why did our gross margin change this month?”
Customer AI Advisor
Analyzes support activity, account signals and retention risk.
e.g. “Which customers are showing signs of churn?”
What this looks like in different businesses.
Illustrative examples of how the same architecture applies across industries — not client results.
“Which shipments are at risk this week?”
- Delayed carrier activity
- Warehouse backlog
- Insufficient capacity
- Weather disruption
“Why was production lower last week?”
- Machine downtime
- Material shortage
- Staffing gaps
- Rework
“Which properties need attention today?”
- Unresolved maintenance
- Rent delinquency
- Lease expirations
- Unusual complaint volume
AI Advisor vs. AI Automation vs. AI Agent
These solve different problems. Most companies should know which one they actually need before they buy either.
AI Advisor
A human makes the decision.
AI Automation
Automates a predefined, repetitive workflow.
AI Agent
May use tools to complete an action inside defined rules.
Many companies should start with an Advisor before giving AI permission to take significant actions on its own.
See our AI Automation serviceAdvisor maturity model
Start with intelligence. Add autonomy when it makes sense.
Not every business should start at the same level — and most should not start at Level 3.
Level 1
Advisor
Level 2
Advisor + Approval
Level 3
Agent
Answers are more useful when you can see where they came from.
The biggest objection to any AI system that touches company data is trust. We address it with source attribution, permission-aware access, and human review — not a claim that the model is always right.
- Every answer can show the sources it drew from
- Access is scoped to what the Advisor is authorized to read
- Most deployments start read-only
- A person reviews and approves before anything is acted on
Revenue decreased 8.3%.
- Commercial segment down
- Product B cancellations up
- Renewal rate down
How we build an Advisor
Six steps, starting with the decisions your team actually needs to make — not the technology.
- 01
01 — Identify the decisions
What does your team repeatedly need to understand or decide?
- 02
02 — Map the information
Where does the data required for those decisions actually live?
- 03
03 — Connect the systems
Connect authorized sources through APIs, databases, documents and integrations.
- 04
04 — Build the intelligence layer
Business terminology, context, permissions, retrieval and reasoning.
- 05
05 — Evaluate the Advisor
Test real questions, edge cases, grounding and answer quality.
- 06
06 — Launch and expand
Start with high-value advisory use cases; add actions when it makes sense.
Questions people actually ask
A custom AI system connected to your company’s own data that answers plain-language questions, explains what changed, and recommends next steps. A person still decides.
AI Advisors
Build the AI Advisor your team needs.
Tell us what your team needs to understand, where the information lives today, and which decisions take too long — we will take it from there.