Most organisations are not short of AI tools. They are short of AI-native operating models. We help leaders assess readiness, redesign workflows, strengthen governance, and execute transformation — and we run the Agentic PMO platform that does the delivery work.
Advisory & Agentic PMO — nothing else. Two things, done properly.
Practice areas: AI-native operating model design · Agentic PMO · AI governance frameworks · Organisational readiness diagnostics
Sector experience: capital projects & EPC, manufacturing MSMEs, public sector, professional services
With co-founders Alok Matta and Harbinder N.
Advisory sets the direction — diagnostic, operating model, governance. Agentic PMO is the delivery engine that runs underneath it.
A systematic assessment across 10 dimensions — strategy, workflows, knowledge, data, people, governance, technology, trust, economics, and learning. You get a scored maturity profile, gap analysis, and priority actions.
Maturity scorecard across all 10 dimensions · Benchmark against sector peers · Prioritised gap analysis · 90-day quick-win roadmap
We redesign workflows, decision rights, knowledge flows, and governance structures so your organisation operates natively in an AI-enabled environment — not as a legacy machine with AI bolted on.
Governance framework · Workflow architecture · Knowledge operations design · Human-AI collaboration model · Change roadmap
Three AI agents — Plan, Monitor, Alert — generate and govern the plan, then keep watching it. A small senior team does the work of a 10+ person PMO. Deployed standalone or as an intelligence layer over Primavera P6 / MS Project.
AI agents that plan, monitor, and alert — turning strategy into governed execution. Smaller teams, smarter delivery, across any industry.
Working software you can log into: portfolio and project dashboards, the generated WBS, a Kanban execution board, the risk register, KPI dashboards, monitoring rules, and the full alert and audit trail. We will walk you through a live, fully populated project in 30 minutes.
agenticpmo.tech
Access is provisioned per engagement · request a walkthrough
One application for the whole delivery loop: plan it, run it, watch it, prove it.
Platform on your infrastructure + domain pack for your sector + (for large programmes) a custom integration layer to your scheduling and document tools. Live in 8–12 days after the onboarding questionnaire.
The traditional model needs 10–20-person PMO teams to manually track tasks, update status, chase stakeholders, and produce reports. And they still miss what matters.
Three AI agents — Plan, Monitor, Alert — each specialised, each reading industry-specific domain packs. A 3-person team (PM + 2 domain experts) does the work of a 10+ person PMO.
Three specialised AI agents — each with a distinct job, sharing the same domain knowledge.
Takes a project brief + domain pack → generates 100+ tasks across 8 phases with milestones, dependencies, and discipline assignments. Catches scope gaps automatically: "You have tasks for gasifier procurement but nothing for biomass storage yard."
Uses an LLM — your choice of provider (OpenAI, Anthropic, OpenRouter). Falls back to template plans if no LLM key is configured.
Runs deterministic checks — no LLM required (fast, predictable, auditable). Reads monitoring rules from the domain pack, runs weekly or on-demand, flags drift from plan, delays, missing deliverables, and risk triggers before they escalate.
Example rule: "If HAZOP close-out < 80% and 4 weeks since completion → flag as critical"
Reviews anomalies detected by the Monitor Agent, escalates based on severity (info → warning → critical), cross-references against the risk register for recommended actions. Resolved alerts are logged for a complete audit trail.
Hybrid design: Plan + Alert use LLMs (creative work). Monitor is rule-based (zero LLM cost). Critical checks are always fast and predictable.
Everything the agents act on is a record in the platform, keyed to the domain pack. Nothing important lives in a tracker someone keeps privately.
Milestones are modelled as zero-hour tasks carrying the milestone priority — not as a separate entity living outside the plan.
Every project, task and risk can carry its external ID from your ERP or scheduling tool (WBS element, MS Project UID, Jira key). That is what makes the integration layer, the sync log, and cost or schedule reconciliation possible — and auditable.
A domain pack is a folder of JSON and Markdown files. No database, no special tooling — edit in any text editor. Agents read the pack to know what to look for in your specific industry.
| EPC Capital Projects | 165 tasks · 85 risks · 40 KPIs |
| Industry 4.0 Smart Manufacturing | 92 tasks · 41 risks · 26 KPIs |
| Green Methanol (Biomass Gasification) | 104 tasks · 59 risks · 24 KPIs |
| SAF Biomass FT | 131 tasks · 88 risks · 32 KPIs |
New packs created in 2–3 days with one subject-matter expert.
From project brief to active monitoring in 3 clicks.
Define a brief goal and scope
Choose a domain pack — 90% of your WBS is pre-built
Review and adjust with engineers
Deterministic checks against domain rules
Background escalation when thresholds breached
Agents don't replace the project team — they augment. The PM still reviews, adjusts, and approves. But the agent does the busywork of generation, comparison, and flagging.
Agentic PMO adapts to your project scale — no forced migration, no rip-and-replace.
For small to medium projects
Agentic PMO is your primary project management tool. The Plan Agent generates the full WBS, the Kanban board tracks execution, and the Monitor Agent runs weekly checks. Works with any document repository — Google Drive, SharePoint, Dropbox, or a CDE like Autodesk Docs.
Best for: Plant expansions, retrofit projects, pilot plants, green fuel feasibility studies — 12 to 24 month projects.
For large capital projects
Your organisation already runs Primavera P6 or MS Project. Agentic PMO sits alongside these as an intelligence layer — connected via a custom-built integration layer. No migration needed. No data duplication.
Agentic PMO handles: Domain WBS generation, risk flagging, monitoring rules, KPI tracking, scope gap detection, compliance checks, AI-driven risk escalation
Your tools remain: Primavera P6 / MS Project (schedule of record), CDE/DMS (document repository), ERP (budget of record)
Best for: Greenfield EPC plants, large infrastructure projects, multi-year capital programmes
Humans do what humans are best at. AI does what AI is best at. The result: a 3-person team does the work of a 10+ person PMO.
| PMO Director | 1 |
| Project Coordinators | 3–4 |
| Schedulers / Planners | 2–3 |
| Risk Managers | 1–2 |
| Report Writers | 1–2 |
| Document Controllers | 1–2 |
| IT Operations | 1 |
| Total | 10–14 |
| PM / Lead | 1 |
| Coordination | N8N automates |
| Scheduling | Plan Agent + PM |
| Risk Management | Monitor + Alert |
| Reporting | N8N generates |
| Document Control | CDE/DMS handles |
| AI Agent Mgmt | Paperclip |
| IT Operations | Uptime Kuma |
| Total | 3–4 |
A PMO that acts without a trail is a liability. Agentic PMO is built so that anything an agent flags, recommends, or changes can be reconstructed afterwards.
The Monitor Agent is deterministic and rule-based: same inputs, same outputs. Any flag can be traced to the exact rule, threshold, and data point that produced it — which is what makes it usable as evidence in a client or regulator conversation.
Agents recommend. No task is closed, no date is moved, no cost is committed, and no escalation reaches a client by an agent acting alone. Every alert carries a recommended action, waits for a human decision, and logs both.
What reaches an LLM is task titles, descriptions, and domain pack rules — no PII, no financial data, no documents. Documents never leave your CDE: the platform reads metadata and status, not drawings and specifications.
REST API-first design. We don't sell a pre-built "Primavera connector." We bring our API and build the integration layer that fits your specific stack — in days, not months.
Your Stack Integration Layer Agentic PMO
Primavera P6 ──▶ Sync Engine ──▶ REST API
MS Project ──▶ (custom Python) ──▶ Monitor Agent
CDE / DMS ──▶ Reads schedule ──▶ Alert Agent
ERP / Cost ──▶ Transforms data Webhooks ──▶
Pushes to PMO Slack/Teams
This 24-question form scopes your integration — from tool stack and custom fields to sync cadence and infrastructure. Fill it out with your scheduling lead and IT/API owner (~30–45 minutes), and we'll have everything we need to build your custom connector.
Interactive web form — auto-saves your progress, exports to Markdown when done. Or download the static version.
Open Interactive Form →Build estimate: 8–12 days after questionnaire complete
The Gujarat Green Methanol Plant — a fully populated EPC demo project running on Agentic PMO.
Demo generated in 30 seconds. Apply the EPC domain pack, populate with demo data, run Monitor + Alert agents — and see real alerts, risks, and KPIs in your dashboard. This is the same populated project we open when we walk you through agenticpmo.tech.
We would rather you knew the edges of the platform before a pilot than after one.
Every engagement is assessed across the same comprehensive framework — from strategy to governance to learning.
AI vision, executive commitment, and strategic alignment. How AI-native thinking is embedded in corporate strategy.
Process redesign for human-AI collaboration. Agentic workflows that eliminate bottlenecks and amplify human capability.
How organisational knowledge is captured, structured, and served. The queryable state of business intelligence.
Data architecture, quality, accessibility, and governance. The fuel for AI-native operations.
Talent strategy, AI literacy, culture of experimentation, and human-AI collaboration readiness.
Decision rights, accountability structures, ethics frameworks, and regulatory compliance for AI systems.
AI infrastructure, platform maturity, integration patterns, and technical debt management.
Stakeholder trust, transparency practices, explainability, and responsible AI commitments.
Investment models, ROI measurement, cost optimisation, and value capture from AI initiatives.
Organisational learning loops, feedback mechanisms, continuous improvement, and adaptation velocity.
A structured three-phase approach that moves from assessment to action — always calibrated to your organisation's context and capacity.
10-dimension maturity assessment, stakeholder interviews, document analysis, and ecosystem mapping. You get a clear picture of where you stand and what needs attention first.
Governance framework, workflow architecture, knowledge operations model, and transformation roadmap. Built for your sector, your scale, and your risk posture.
Partner-led implementation, 90-day sprint cycles, real-time dashboards, and continuous maturity tracking — with Agentic PMO carrying the plan, monitoring and reporting load.
AI Native Advisory is a deliberately small, senior practice. You work directly with the founders — there is no layer between you and the people doing the work.
Author of The AI-Native State and creator of the 10-Dimension AI-Native Maturity Model. Leads the advisory practice — readiness diagnostics, operating model redesign, and AI governance for capital project and enterprise clients.
Whether you run capital projects, a manufacturing operation, or a public sector institution — we'd like to understand your context and explore how we can help. If you want to see Agentic PMO first, start with the platform.
Prashant Nikam, Founder — with co-founders Alok Matta and Harbinder N.