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 — from strategy to delivery.
Practice areas: AI-native operating model design · Agentic PMO · AI governance frameworks · Organisational readiness diagnostics · Industry 4.0 transformation
Sector experience: FTSE 350, manufacturing MSMEs, public sector, professional services
From diagnostic to roadmap to implementation — a structured approach to becoming AI-native.
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
AI agents that plan, monitor, and alert — turning strategy into governed execution. Smaller teams, smarter delivery, across any industry.
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.
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, Industry 4.0 engagements — 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 |
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.
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.
We work with FTSE 100 and FTSE 250 leaders to assess AI-native maturity and design transformation programmes that deliver measurable business outcomes.
The UK's AI adoption paradox: high investment, low organisational transformation. Most FTSE 100 and FTSE 250 companies deploy AI tactically — but few have redesigned their operating models for the age of human-led machine intelligence. Being AI-native means the operating model itself — workflows, decisions, knowledge flows, governance — is built around human-led machine intelligence, not just bolting AI onto legacy processes.
We help close this gap through structured diagnostics, governance frameworks, and delivery roadmaps tailored to the scale and regulatory complexity of FTSE 350 enterprises.
Phase 1: Readiness diagnostic (4-6 weeks) — scored maturity profile across 10 dimensions
Phase 2: Governance pack + workflow redesign (6-8 weeks) — designed for your operating context
Phase 3: 90-day transformation roadmap with partner-led implementation support
The FTSE 350 are the UK's economic backbone — 100 blue-chip leaders and 250 mid-cap growth engines. They have the resources to lead and a pressing need to transform. Our methodology is built for their scale: regulated, board-governed, and multi-stakeholder.
We combine diagnostic rigour with practical delivery. Every engagement starts with data — not opinion. We map your current maturity, identify the highest-leverage gaps, and design a governed programme to close them. We work alongside your teams, not in a separate room.
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. We stay engaged through the first delivery cycle to ensure momentum.
Whether you're a FTSE 350 leader, an MSME manufacturer, or a public sector institution — we'd like to understand your context and explore how we can help.
Prashant Nikam, Founder — AI Native Organisations