You have AI pilots, and none in production.
What it costsBudget spent, and nothing to show the board.
What changesOne workflow at a time, with a baseline, a test set and pass criteria before go-live.
Agentic OS · AgentsEnterprise AI Transformation
From AI pilots to AI that runs real work.
We find where AI pays back across your enterprise, fix the data and process underneath it, and put agents and automation into production inside your own stack, with approvals, an audit trail and a measured result for each workflow.
Four phases, a decision at each
- AssessRoadmap and business case
- DesignArchitecture and controls, signed off
- EngineerGoverned agents in production
- ScaleAdoption, then the next workflow
Each phase ends with something you can evaluate, fund or stop
Written for
CEOs, COOs and CIOs who must show a return on AI, and the legal, risk and audit leaders who sign it off
The question you came with
We have pilots everywhere. How do we get one into production and prove it paid back?
What is stalling AI in your company
If two of these are true, the rest of this page is about you.
Every team bought its own AI tool.
What it costsOverlapping spend, and customer data in tools nobody approved.
What changesOne approved stack: the provider, region and access rules agreed with your security team.
Agentic OS · ContextNobody can say what AI has returned.
What it costsThe next budget round becomes an argument about opinion.
What changesEvery workflow tied to a baseline and a measured result, reported each month.
Agentic OS · PulsePeople were given tools, not a new way of working.
What it costsUsage fades after the first month.
What changesRole-based training, recorded, with adoption checked weeks after go-live.
Agentic OS · LoopFour phases, a decision at each
You are never asked to commit to the whole programme up front. Each phase ends with something you can evaluate, fund or stop.
- Phase 01 · Assess
Readiness and opportunity
Where AI creates measurable value, and what has to be true to capture it. You get a roadmap and a business case.
- Phase 02 · Design
Architecture and operating model
The system design, the governance model and the first workflows to hand to agents. You get a signed-off architecture and controls.
- Phase 03 · Engineer
Build, integrate, evaluate
Agents built against your systems, tested against your acceptance criteria, and released with approvals. You get governed agents in production.
- Phase 04 · Scale
Adoption and expansion
Team enablement, monitoring, and the next workflows, funded by the measured results of the last. You get a regular value report.
Across every function
The same programme, foundations and controls, applied wherever the work is. These are the first agents we usually see in each function.
- Revenue
Sales, marketing and customer success
First agents: Account research, inbound qualification, CRM hygiene, renewal prep.
- Operations
Orders, fulfilment and supply chain
First agents: Document and order intake, exception triage, supplier follow-up.
- Finance
Payables, receivables, close and reporting
First agents: Invoice matching, collections follow-up, variance commentary.
- People
HR, recruiting and onboarding
First agents: Policy answers, interview notes, onboarding follow-through.
- Customer service
Support and service desks
First agents: Ticket triage, replies drafted from approved content, escalation summaries.
- IT and knowledge
Systems, security and internal knowledge
First agents: Search across documents with permissions respected, access requests, security questionnaires.
- Legal
Contracts, matters and obligations
First agents: Contract first reads against your playbook, clause comparison, obligation and renewal tracking.
- Risk and compliance
Policy, regulatory change and controls
First agents: Policy pre-checks on outgoing material, regulatory change summaries, control monitoring.
- Internal audit
Assurance over controls and processes
First agents: Evidence collection, sample testing, exceptions logged with the full audit trail.
Every agent drafts, checks or flags. A named person approves anything that matters, and every run is logged.
What the programme includes
AI Transformation is the programme and the agent platform. The foundations make agents trustworthy, the build services create the agents for each function, and the revenue agents serve sales and marketing.
- Agents
Governed agents
Built as part of this programme: research, intake, drafts, triage and updates, each approved by a person. See them further down this page.
- Foundations
Data and AI Foundations
One trusted source of data, so agents, dashboards and forecasts agree.
- Foundations
Business Systems
The CRM, ERP, tax, HR and field systems agents act in, set up and connected.
- Foundations
Cloud and AI Infrastructure
Where AI runs: in-region, private where needed, and cost-controlled.
- Foundations
Security, Privacy and AI Governance
The policy, consent checks and evidence legal, risk and audit sign off.
- Build
Intelligent Automation
Rules and bots for the routine steps, agents for the exceptions.
- Build
Conversational and Voice AI
Chat, WhatsApp and voice agents for customers, in their language.
- Build
Web Transformation
A website AI assistants can read and cite, feeding every enquiry into the same workflows.
- Build
AI Product Engineering
AI products, apps and internal platforms, from first prototype to scale.
- Build
Industrial AI and IoT
Vision, sensors and predictive maintenance on the floor, the fleet and the site.
- Revenue
Revenue Operations
One clean CRM record and agreed definitions before any revenue agent is switched on.
- Revenue
GTM Engineering
The signals, enrichment and routing that give agents something true to act on.
- Operating model
Pegasus Agentic OS
Six layers that show where AI belongs in your business, and in what order to build it.
How every agent runs
The same loop for every agent we build: a trigger, the work, a person, a record, and a weekly check on quality.
A person where it matters
Anything sent to a customer, any price and any risky change is approved by a named person. You decide what an agent may do on its own.
Sources shown
Every brief, summary and draft links to what it was built from, so a rep can check it in seconds.
Logged and measured
Every run is recorded. Each agent has a test set built from your real cases and is re-checked after every change.
Your data, your model
Agents run in your stack, through the model provider and region your security team approves. Prompts and evaluations live in your repo.
Built to pass legal, risk and audit sign-off
Enterprise AI needs more than a sponsor. These are the people who approve it, and what each of them gets before the first agent goes live.
General Counsel
Owns contracts and legal risk
- Judged on
- No surprises in what the company signs or says.
- What changes for them
- Every agent that drafts or sends works from approved wording, with a named approver and a record.
Chief Risk or Compliance Officer
Owns policy and regulatory exposure
- Judged on
- Controls that hold up in a regulator review.
- What changes for them
- Rules, approval steps and data handling designed with you before anything goes live.
Head of Internal Audit
Owns assurance over controls
- Judged on
- Evidence that is complete and easy to test.
- What changes for them
- Every agent run logged with inputs, sources, approver and outcome, exportable on request.
CIO or CISO
Owns systems, data and security
- Judged on
- No company data in unapproved tools.
- What changes for them
- Agents run in your stack, through the model provider and region your security team approves.
Agents we build, one per function
Pick a function to see what its agent does. Every agent drafts, checks or flags. A named person approves anything that matters, and every run is logged.
Invoices matched to orders and receipts. Only the gaps reach a person.
Every contract compared to your playbook. Lawyers start from a redline, not page one.
Outgoing material and new rules checked before they reach the business.
Control evidence gathered and sampled on schedule. Exceptions logged for the audit team.
Delays and exceptions caught when they happen, with the customer update drafted.
Tickets triaged and answered in draft from approved content. Anything sensitive goes to a person.
Agents across the enterprise
Agent families.
One way of working.
Six families of agents across operations, finance, people, service and IT, legal and risk, and revenue. Every one follows the same loop, the same review rules and the same logging.
- 01ReadPull the facts from your systems
- 02CheckTest them against your rules
- 03DraftPrepare the work, with sources
- 05LogEvery step recorded
01
Operations agents
Keep orders, suppliers and exceptions moving.
- Read
Document intake
Orders, forms and attachments read, checked for missing fields and written to the system of record.
- Check
Exception triage
Late, short or blocked items flagged to the owner with the context attached.
- Draft
Supplier follow-up
Confirmations chased and delays matched to the orders they affect.
02
Finance agents
Match, chase and explain, with finance signing off.
- Check
Invoice matching
Invoices matched to orders and receipts; mismatches sent for review.
Collections follow-up
Reminders drafted in your tone, sent only after approval.
- Draft
Close commentary
Variances explained in a first draft for the finance lead to edit.
03
People agents
Answer, prepare and follow through for HR.
- Read
Policy answers
Questions answered from your handbook, with the source linked.
- Draft
Interview notes
Notes and scorecards drafted for the hiring manager to confirm.
- Log
Onboarding follow-through
New starters tracked against their first-month plan.
04
Service and IT agents
Resolve routine requests, escalate the rest.
- Draft
Ticket triage
Requests classified, routed and answered in draft from approved content.
- Read
Internal search
Answers from your documents, respecting who is allowed to see what.
Voice and chat agents
Calls and chats answered end to end, with a clean hand-off to a person.
05
Legal, risk and compliance agents
Only the exceptions reach a lawyer or auditor.
- Check
Contract first read
Clauses compared to your playbook, with a first redline for the lawyer to edit.
- Check
Policy pre-check
Outgoing material checked against your rule set before compliance sees it.
- Log
Audit evidence
Control evidence gathered and sampled, exceptions logged for the audit team.
06
Revenue agents
Find the right accounts and keep the pipeline honest.
- Read
Account and meeting briefs
Public sources and CRM history condensed into a brief before each call, with citations.
- Log
CRM clean-up and call notes
Duplicates fixed, call summaries and next steps written to the CRM.
- Check
Renewal and expansion signals
Health, usage and growth signals flagged to the account owner.
- Sources shown on every output
- A named person approves what matters
- Every run logged and measured
- Built in your stack, owned by you
Enterprise AI transformation, by industry
The programme is the same everywhere. The first workflows to hand to agents are not.
What we build
The capability on the left, what your team would notice on the right.
- Readiness
AI readiness review
Where AI creates measurable value in your operation, and what has to be true in data, systems and people to capture it.
- Roadmap
Use cases ranked by value and effort
A short list with a business case for each, so the first build is the one most likely to pay back.
- Foundations
Data and knowledge agents can trust
Records cleaned, documents structured and permissions respected, before any agent reads them.
- Build
Agents and automation in production
Built inside your tools, reviewed by people, logged and measured. Not a demo.
- Integration
LLMs inside the apps you already run
Language models added to existing applications and workflows, so people get AI where they already work.
- Orchestration
Agent orchestration and MCP connectors
One integration layer that connects agents to your systems through MCP and APIs, with permissions enforced in one place.
- Models
Private and fine-tuned models
Small language models fine-tuned on your domain and hosted privately where data cannot leave your control.
- Governance
Approvals, audit trail and evaluation
A named approver for anything that matters, every run recorded, and quality re-checked after every change.
- Adoption
Training and change
A short recorded walkthrough for each role, and a check on who is actually using it.
What stays yours
We build and hand over
- The roadmap and business case, written down
- Agents, prompts and evaluations, in your repo
- Runbooks and recorded training
You keep and control
- Your data, in your systems
- Your model provider, billed to you directly
- 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.
Find your agent
AI Transformation is the programme and the agent platform: orchestration, governance, evaluation and the controls every agent shares. The agents for each function are built through the services that know that work. Start from your function.
FinanceFinance Agent: AP and ReconciliationIntelligent AutomationCustomer serviceService Resolution AgentConversational and Voice AICalls and WhatsAppAI Voice Agent LaunchConversational and Voice AIIT and employeesIT Service Desk AgentIntelligent AutomationSales and marketingAlways-on ABM: Meetings BookedGTM EngineeringKnowledge and policyKnowledge Assistant in 30 DaysAI TransformationLegal, risk and auditAI Governance and ISO 42001 ReadinessSecurity, Privacy and AI GovernanceQuality and operationsVisual Quality Inspection PilotIndustrial AI and IoT
Fixed-scope offers
Fixed scope, a set timeline and a deliverable you keep. The quickest way to start.
Assess
AI Transformation Review
A ranked list of workflows to hand to agents, with a business case for each
- Time
- 2 to 3 weeks
- Price
- $1,500, credited to month one
- For
- CEOs, COOs and CIOs
The problem. You have AI ideas and pilots, but no agreed view of where AI pays back first or what has to be fixed before it can.
What happens, week by week
- Week 1Interviews and systems review
Short interviews with business, technology, finance and risk leaders, and a look at the systems and data each workflow depends on.
- Week 2Workflows ranked by value and effort
Candidate workflows scored on volume, value, data readiness and risk, against the six layers of Pegasus Agentic OS.
- Week 3Roadmap and business case
The first three workflows, the foundations they need, and a business case built from your own baseline.
You get
- A ranked workflow shortlist
- A business case for the top three
- A foundations gap list (data, systems, controls)
- A phased roadmap and budget range
Who signs off
The executive sponsor, with finance confirming the baseline
What we need from you
Two to three hours of leaders' time, and read access to the main systems
Pilot
Agent Pilot to Production
One workflow live in production, measured against a baseline
- Time
- 90 days
- Price
- Fixed fee, agreed on a call
- For
- CIOs, CTOs and the business owner of one workflow
The problem. A pilot worked in the demo, and has not reached real work: no owner, no baseline, data that only works on a sample, and no controls.
What happens, week by week
- Days 1 to 15Choose and measure
Pick one high-volume workflow with a business owner, and measure today's cost, time and error rate.
- Days 16 to 30Fix the data it reads
Only the fields and records this workflow needs, each with a named owner and a quality rule.
- Days 31 to 60Build with controls
The agent built in your stack, with permissions, approval points and logging agreed with risk and audit.
- Days 61 to 90Run in parallel, then go live
Agent and team work side by side on live cases, then the workflow switches over with reporting against the baseline.
You get
- One agent in production
- Baseline and monthly results report
- Risk tier, approvals and audit trail
- A repeatable pattern for the next workflow
Who signs off
CIO or CTO, the workflow owner, and risk
What we need from you
A named business owner, system access, and a weekly 30-minute review
Run
Pegasus Run
Agents, bots and data platforms monitored, fixed and improved every month
- Time
- Monthly, cancellable
- Price
- Monthly fee, set by scope
- For
- CIOs and the owners of agents, automations and data platforms in production
The problem. AI in production drifts: data changes, systems update, models change, and nobody owns the improvements.
What happens, week by week
- Every dayMonitoring
Agent runs, bots, pipelines and dashboards watched, with failures fixed against agreed response times.
- Every weekReview and tune
Agent decisions sampled and scored, prompts and rules improved, and exceptions reviewed with your team.
- Every monthValue report
Results against baseline for each workflow, incidents, changes made, and the next improvements proposed.
- Every quarterRoadmap review
The next workflows to automate, funded by the measured results of the last.
You get
- Monitoring and incident response
- Weekly evaluation and tuning
- Monthly value report
- Quarterly roadmap review
Who signs off
CIO, and the owner of each workflow
What we need from you
A named contact, and access to monitoring and the systems involved
Pilot
Knowledge Assistant in 30 Days
An assistant that answers from your own policies, SOPs and contracts, with sources, in your languages
- Time
- 30 days
- Price
- Fixed fee, agreed on a call
- For
- HR, legal, operations and service leaders
The problem. People spend hours searching shared drives and asking colleagues, and answers differ depending on who you ask.
What happens, week by week
- Days 1 to 7Choose the knowledge
Pick one area, such as HR policies, SOPs, contracts or product documents, and agree who may see what.
- Days 8 to 18Prepare and connect
Documents cleaned, structured and indexed with your access permissions kept, in the languages your teams use.
- Days 19 to 25Test against real questions
Answers checked against a test set of real questions, with every answer citing its source.
- Days 26 to 30Launch in Teams or the web
Live where people already work, with usage and unanswered questions reported weekly.
You get
- A live assistant for one knowledge area
- Answers that cite the source document
- Access permissions respected
- A test set and weekly quality report
Who signs off
The owner of the knowledge area, and IT for access
What we need from you
The documents, a list of real questions, and a reviewer for answers
More ways to start, across the programme
Each offer sits on the service page it belongs to, with the week-by-week plan.
| Offer | Stage | Time | Service |
|---|---|---|---|
| AI-Ready Data Assessment | Assess | 3 weeks | Data and AI Foundations |
| Finance Agent: AP and Reconciliation | Pilot | 8 to 10 weeks | Intelligent Automation |
| AI Voice Agent Launch | Pilot | 6 weeks | Conversational and Voice AI |
| AI Governance and ISO 42001 Readiness | Assess | 4 weeks | Security, Privacy and AI Governance |
| Service Resolution Agent | Pilot | 8 weeks | Conversational and Voice AI |
| IT Service Desk Agent | Pilot | 8 weeks | Intelligent Automation |
| Microsoft 365 Copilot Rollout | Pilot | 6 to 8 weeks | Security, Privacy and AI Governance |
| E-invoicing Compliance with AI Extraction | Pilot | 6 to 10 weeks | Business Systems |
| Private and Sovereign AI Setup | Pilot | 6 to 8 weeks | Cloud and AI Infrastructure |
| CRM Rescue for AI | Pilot | 6 to 8 weeks | Revenue Operations |
| Process Mining to Automation Roadmap | Assess | 4 weeks | Intelligent Automation |
| AI Product Sprint | Assess | 4 to 6 weeks | AI Product Engineering |
| Visual Quality Inspection Pilot | Pilot | 8 to 10 weeks | Industrial AI and IoT |
| Always-on ABM: Meetings Booked | Pilot | 6 weeks to launch, then monthly | GTM Engineering |
| AI Search Visibility Audit | Assess | 3 weeks | Web Transformation |
| Claims and Denials Agent | Pilot | 10 weeks | Intelligent Automation |
Questions
How do the services fit together?
AI Transformation is the programme, and the agents are built inside it. The foundations services make your data and controls ready, the build services change how work and customer contact happen, and the revenue services give sales agents something true to act on. Pegasus Agentic OS is the operating model behind all of it.
Where do we start?
With a fixed-fee AI transformation review. It names the first workflow worth handing to an agent, and the foundations that have to be fixed first.
Do we have to do the whole programme?
No. Each phase ends with something you can evaluate, fund or stop, and each service stands on its own.
Is this only for sales and marketing?
No. The programme covers revenue, operations, finance, legal, risk and compliance, internal audit, people, customer service and IT. The review picks the first workflow by value and readiness, wherever it sits.
How do we know it is working?
Each workflow has a baseline and a test set built from your real cases. You see the results before go-live, and a written report each month after.
Will agents email prospects on their own?
Only if you decide they should, and only after they have earned it on a measured test set. By default every outbound message is drafted for a person to approve.
How do you know an agent is good enough?
Each one has a test set built from your real cases with clear pass criteria. You see the results before go-live, and they are re-run after every change.
What does it cost to run?
Model usage is billed by the provider directly to you. We estimate it in the scope and set alerts, so it cannot surprise you.
Keep going
Where AI Transformation fits your industry, what to read next, and the tools involved.
Industries we do this in
Playbooks
Tools we review for this
Find your path through Pegasus
Six people usually sign off on enterprise AI. Pick yours and we show you what to read, in order, and what to ask us.
Executive sponsorCEO, COO, business unit headTransformation leadChief AI, digital or transformation officerTechnology ownerCIO, CTO, head of data, enterprise architectLegal, risk and auditGeneral counsel, CRO, CISO, internal auditFunctional leaderRevenue, finance, people, operations or service leadProcurementProcurement, vendor management, PMO
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.