Every team has a different number for the same thing.
What it costsMeetings start with which report is right.
What changesAgreed definitions in one semantic layer, owned by named people.
Agentic OS · ContextAI Transformation · Data and AI Foundations
The data your agents read, made worth trusting.
Every agent, dashboard and forecast is only as good as the data underneath it. We build the platform, pipelines and definitions that give your enterprise one trusted source, then put reporting, forecasting and AI on top of it.
Six parts, one source of truth
- AssessBI maturity and a roadmap
- PlatformFabric, Snowflake, BigQuery or Databricks
- PipelinesEvery system feeding one model
- GovernDefinitions, quality and ownership
- ReportDashboards people trust
- PredictForecasting and ML, in production
Built in your cloud, documented and handed over
Written for
CIOs, CDOs, CFOs and heads of analytics who need one trusted number before AI can act on it
The question you came with
Why do our dashboards disagree, and what does our data need before agents can use it?
Part of Enterprise AI Transformation
The foundation. Agents, dashboards and forecasts all read the same governed data. Fixing it for one workflow is usually the first step of the programme.
See the whole programmeWhat is holding your data back
If two of these are true, the rest of this page is about you.
Data lives in a dozen systems nobody joins up.
What it costsAnalysts spend days pulling spreadsheets together.
What changesPipelines that load every source into one platform, on a schedule, with checks.
Agentic OS · SignalThe AI pilot worked on a sample and failed on real data.
What it costsMonths spent, and no agent in production.
What changesData quality checks and ownership on the fields each agent reads, before it is built.
Agentic OS · AgentsDashboards exist, and nobody acts on them.
What it costsDecisions still run on instinct.
What changesReports built around the decision each role makes, with alerts when something moves.
Agentic OS · PulseHow data becomes a decision
The same path for a board report, a forecast or an AI agent.
- Step 01 · Source
Every system connected
ERP, CRM, finance, HR and operations data loaded on a schedule, with failures flagged.
- Step 02 · Model
One model of the business
Customers, products, orders and people defined once, with the history kept.
- Step 03 · Govern
Quality and ownership
Rules check the data every load, and every key field has an owner who fixes it.
- Step 04 · Use
Reports, forecasts and agents
Dashboards, models and agents all read the same governed data, so their answers agree.
What we build
The capability on the left, what your team would notice on the right.
- Strategy
BI maturity assessment and roadmap
Where you are, what to fix first, and a plan tied to decisions the business actually makes.
- Platform
Modern data platform
Microsoft Fabric, Snowflake, BigQuery, Databricks or Redshift, set up in your cloud with access control from day one.
- Pipelines
ETL and real-time integration
Batch and streaming pipelines from ERP, CRM, finance and operations systems, monitored and documented.
- Governance
Data quality and master data
Definitions, owners, quality rules and lineage, so everyone and every agent reads the same truth.
- Reporting
Dashboards, self-service and embedded analytics
Power BI, Tableau, Looker or Zoho Analytics, with a semantic layer and training, and analytics embedded inside your own SaaS product.
- Modernise
Legacy BI migration
Old reports and on-premise BI moved to modern cloud tools, with the numbers reconciled before the old system is switched off.
- Advanced analytics
Forecasting, risk scoring and ML
Demand, churn and sales forecasts, fraud and risk scoring, and text analytics, deployed and monitored, not left in a notebook.
What stays yours
We build and hand over
- Data models, pipelines and their documentation
- Semantic layer, dashboards and definitions
- Models, evaluation results and runbooks
You keep and control
- Your data, in your cloud tenancy
- Your platform and licence contracts
- Admin access, in your name
How an engagement runs
The same four steps whatever we build. The first one is small, fixed-fee and useful on its own.
Start with a fixed-scope offer
A fixed scope, a set timeline and a deliverable you keep. The quickest way to start.
Assess
AI-Ready Data Assessment
A clear view of which data blocks which AI use case, and the plan to fix it
- Time
- 3 weeks
- Price
- Fixed fee, agreed on a call
- For
- CIOs, CDOs and heads of analytics
The problem. AI plans assume the data is ready. It usually is not, and nobody knows which gaps matter most.
What happens, week by week
- Week 1Map the sources
The systems, tables and fields behind your priority AI use cases and key reports.
- Week 2Test the quality
Completeness, duplicates, freshness and definitions checked on the data each use case reads.
- Week 3Plan the fix
Gaps ranked by impact, with owners, a platform recommendation if needed, and a dated plan.
You get
- A source and lineage map
- A data quality scorecard per use case
- A ranked fix list with owners
- A platform recommendation, with reasons
Who signs off
CIO or CDO, with the data owners
What we need from you
Read access to the main systems and an hour with each data owner
Not sure where to start? Begin with the AI Transformation Review: two to three weeks, $1,500, credited to month one.
Questions
Do we need a new data platform?
Not always. The assessment says whether your current tools can do the job, and recommends a platform with reasons if they cannot.
Which platforms do you work with?
Microsoft Fabric, Snowflake, BigQuery, Databricks and Redshift, with Power BI, Tableau, Looker or Zoho Analytics on top.
How does this connect to AI Transformation?
Agents read the same governed data as your dashboards. Fixing the data for one workflow is usually the first step of the programme.
Can you run the platform after go-live?
Yes. Pegasus Run covers monitoring, fixes and improvements each month, and you can stop it at any time.
Keep going
Where Data and AI Foundations fits your industry, what to read next, and the tools involved.
Industries we do this in
Playbooks
Tools we review for this
AI transformation for the agentic engine era
Start with a fixed-fee AI transformation review. We map your systems, data and teams against the six layers of Pegasus Agentic OS and name the first workflows to hand to agents. You keep the findings whether or not you work with us.