Build a persistent knowledge base about your products, stakeholders, and business context. Every project you run draws from it — producing sprint plans, architecture playbooks, and engineering specs that are actually specific to your company.
Most tools are useful for one session. They forget everything by the next. That's not a PM tool — that's a search engine with a chat window.
They write PRDs from scratch in seconds. No memory of your products, your domain, or your past decisions.
They retrieve and summarize what you've already written. They can't reason about what's missing or plan what's next.
They cluster customer signals and surface themes. But they can't bridge discovery to architecture, sprint planning, or engineering handoff.
A persistent knowledge base that grows with each document, interview, and project. Every output draws from structured knowledge of your domain.
Before any project, CeremonyAI builds a structured understanding of who you are as a PM — your domain, your products, and your stakeholder landscape. Every project after draws from it.
Business model, industry context, competitive landscape, success metrics, risk profile, and quantified business cases. The AI understands your market — not just your feature list.
Every product you own, its tech stack, constraints, integration surface, and history. Plans reference your actual architecture — not hypothetical stacks.
Decision-makers, approval chains, budget owners, technical sponsors, and their priorities. Sprint plans account for real org dynamics, not generic roles.
CeremonyAI stores PM knowledge as an entity relationship graph — not a flat document. Every field connects to the others. Hover any node to explore relationships.
Every field in the knowledge base has a confidence score. Green means fully captured (70%+). Amber means partial. Red means missing. You see exactly what the AI knows and what it's guessing — before it plans your sprint.
Run a targeted Gap Interview at any time. CeremonyAI identifies low-confidence fields, asks focused questions, and updates the knowledge base. Coverage improves. Outputs get sharper. No full re-onboarding needed.
"Claude Code gave engineers an AI that understands their entire codebase.
CeremonyAI gives PMs an AI that understands their entire product domain."
Just as a great CLAUDE.md makes an AI coding assistant dramatically more effective, a rich CeremonyAI knowledge base makes every planning output dramatically more precise. The knowledge base is the product. Everything else flows from it.
A continuous loop: build knowledge, plan projects, hand off to engineers, and feed lessons back into the knowledge base.
A guided onboarding interview plus document uploads build a structured profile of your domain, products, and stakeholders. Coverage metrics show exactly what the AI knows. Gap interviews fill what's missing.
Describe your project idea — rough is fine. CeremonyAI already knows your domain, so the brainstorm skips the "what does your company do?" stage and goes straight to sharpening your idea with web research and market context.
AI agents ask the questions you hadn't considered — tailored to your domain and your stakeholders. Out comes a structured charter with objectives, scope, risks, milestones, and success metrics specific to your business.
AI classifies your technical approach (LLM / ML / Hybrid / Rule-based) and dispatches specialists — System Architect, Reliability Engineer, LLM Architect — to produce an architecture playbook grounded in your actual tech stack.
Converts the architecture into release-ready sprint plans. Epics, user stories with testable acceptance criteria, and task breakdowns structured for direct Jira import — with your real stakeholders' names and your real systems in scope.
Generates tool-specific config files so your developers' AI coding assistant has full project context. The architecture decisions, API contracts, and acceptance criteria flow into CLAUDE.md, .cursorrules, and AGENTS.md — ready to use.
CeremonyAI is the intelligence layer at the top of your entire software delivery chain. The PM's knowledge drives the spec. The spec drives the architecture. The architecture drives the sprints. The sprints drive the code. The code ships — with your Jira and version control in the loop.
The knowledge base that powers the spec is the same one that shapes the architecture, generates the sprint tasks, and configures the coding agent. No re-explanation at any handoff point. Every layer inherits the context from the one above.
Sprint plans export directly to Jira — epics, stories, and acceptance criteria mapped to your real project. With Atlassian connected to Bitbucket or GitHub, the generated tasks become the PRs your team delivers. The PM's intent runs all the way to merged code.
Today, CeremonyAI handles everything from knowledge to Code Forge. The Jira integration is live. As AI coding agents mature and version control hooks are connected, the pipeline from PM knowledge to deployed code becomes nearly autonomous.
If you're a PM building LLM applications, ML pipelines, or AI-native features — you've been underserved by every PM tool on the market. CeremonyAI is the first platform with genuine technical depth for AI product work.
CeremonyAI classifies your project's technical approach with rationale — before architects get involved.
Itemized projections: LLM tokens, vector storage, GPU inference, embedding compute, and API costs. Finance gets real numbers, not "it depends."
Architecture playbooks include data strategy — what data you need, availability risks, quality requirements, and pipeline design. Not just software components.
Model drift, data quality, bias, hallucination risks, compliance. Risks that general PM tools don't understand are front and centre in every playbook.
"For the first time, I walked into an architecture review and didn't just understand what we were building — I understood why every decision was made, what it would cost, and what could go wrong. My engineers were surprised I'd thought of things they hadn't."— Product Lead, Enterprise AI Platform
The gap between product thinking and technical execution has never been wider — and the tools haven't caught up.
Every document generator, search tool, and AI assistant starts cold. You describe your company, your products, your stakeholders — and it forgets all of it tomorrow.
You know the business problem, but when engineers talk about RAG pipelines, fine-tuning, and MLOps — decisions get made without your input.
Weeks of workshops, Confluence docs, and alignment meetings just to agree on what we're building. By then, the opportunity has moved.
LLM tokens, GPU compute, storage. Finance wants projections. You have guesses. You can't build a business case on "it depends."
Cursor and Claude Code are incredible builders — but they execute whatever you ask, even if the architecture is wrong. They need the right context to build the right thing.
ChatGPT writes a perfectly generic PRD. It doesn't reference your tech constraints, your actual stakeholders, or your previous decisions. It could be for anyone.
When PMs can't write engineering-ready specs, IT interprets what they think was meant. Three sprints later, the PM sees a demo and says: "That's not what I meant." The team restarts. The cost is invisible on the roadmap — but brutal on the P&L.
Sprint waste isn't a PM problem or an engineering problem. It's a translation problem — and it's the most expensive line item that never appears on a budget. CeremonyAI eliminates it before sprint 1 begins.
| Activity | Without CeremonyAI | With CeremonyAI |
|---|---|---|
| PM context in outputs | Generic boilerplate. Every tool starts cold and forgets your business | Persistent knowledge base — outputs reference your actual products, stakeholders, and constraints |
| Project scoping | 2–3 weeks of workshops, stakeholder interviews, Confluence docs | 10 minutes — brainstorm with context already loaded, get a structured charter |
| Technical approach | "We'll figure it out in sprint 0" — vague, architect-dependent | AI classifies your approach (LLM / ML / Hybrid) with rationale and trade-offs |
| AI cost estimation | Guesswork, T-shirt sizing, "it depends" | Itemised: compute, storage, API calls, LLM tokens — from day 1 |
| Sprint planning | Half-day session, vague stories, teams misaligned on scope | Epics, stories, acceptance criteria — with your real stakeholders in scope |
| Dev handoff | Engineers reinterpret requirements, 50 clarifying questions | ADRs, API contracts, coding tool configs — engineers start building on day 1 |
| Knowledge retention | Lessons from past projects live in Confluence and are never used again | Every project enriches the knowledge base — future plans benefit from past decisions |
CeremonyAI's knowledge flows from product leaders through to engineering teams. Everyone gets what they need from the same source of truth.
Everything procurement, security, and product teams typically ask. Can't find your answer? Email us directly.
CeremonyAI can be deployed within your Enterprise Virtual Private Cloud (VPC), ensuring your data never leaves your controlled environment. All data is encrypted at rest (AES-256) and in transit (TLS 1.3). Your proprietary documents, PM knowledge base, and generated artefacts are fully isolated to your deployment — they are never shared across tenants or used to train any AI model.
No. Your data — including uploaded documents, PM knowledge graphs, and generated sprint plans — is never used to train, fine-tune, or improve any underlying AI model. LLM API calls are made under your own provider agreements (or ours), and leading providers such as Anthropic and Google operate under zero-retention API policies for enterprise tiers. We are happy to provide our data processing agreement (DPA) for your legal team to review.
SOC 2 Type II certification is currently in progress. In the meantime, we are happy to complete your security questionnaire and provide detailed information about our controls. For GDPR, we support data subject rights (erasure, portability, access) and can provide a Data Processing Agreement (DPA). For healthcare organisations requiring HIPAA compliance, please contact us to discuss your requirements.
Dedicated VPC deployment on your preferred cloud provider (AWS, GCP, or Azure) is available today. This gives you the data isolation of on-premise with the operational benefits of cloud infrastructure. True air-gapped on-premise deployment is on our roadmap for organisations with strict data residency requirements. Please contact us to discuss your specific environment and timeline.
SSO via SAML 2.0 and SCIM provisioning for Okta, Azure AD, and other enterprise identity providers is on our near-term roadmap. Enterprise clients can request priority delivery. Please contact us to discuss your IdP requirements and expected rollout timeline.
Yes. CeremonyAI can be configured to leverage your preferred LLM providers — including Anthropic Claude, Google Gemini (via Vertex AI), and open-source models hosted in your environment. If you have existing enterprise agreements with volume discounts or specific data handling terms, we will configure CeremonyAI to route all LLM calls through your accounts rather than ours.
CeremonyAI is a complete, all-inclusive platform. Mermaid.js (used for diagram generation) is open-source under the MIT licence and is bundled — there is no separate licence fee. The RAG infrastructure, vector database, and knowledge graph storage are all included in your subscription. The only variable cost is LLM API usage: if you bring your own provider agreements, those API costs flow through your accounts; if you use CeremonyAI-managed LLM access, it is included in your plan.
Jira integration is live today — sprint plans, epics, stories, and acceptance criteria push directly into your Jira project. Confluence, GitHub/Bitbucket, Linear, and Slack integrations are on our roadmap. Since Confluence shares the Atlassian ecosystem with Jira, it is a near-term priority. Enterprise clients can request priority integration development for specific tools — contact us to discuss your requirements and we can assess feasibility and timeline.
Yes — this is the core purpose of the Onboard PM module. Once a PM's knowledge base is built (through guided interviews, document uploads, and project history), every artefact CeremonyAI generates — brainstorm briefs, architecture playbooks, sprint plans, and Code Forge configs — draws from that structured knowledge. Outputs reference your actual products, your real stakeholders, your existing tech stack, and your business metrics rather than generic industry boilerplate.
Our experts will work with you to evaluate the gap between expected and actual output. This typically involves reviewing knowledge base coverage (which fields are low-confidence or missing), assessing whether additional document uploads or a Gap Interview would improve results, and in some cases a structured feasibility review. We treat output quality gaps as a shared problem to solve — not a support ticket to close.
CeremonyAI uses forced tool-use structured outputs — the LLM is constrained to populate specific schema fields rather than generating free-form text. This significantly reduces hallucination compared to open-ended generation. Additionally, every AI assumption made during artefact generation is surfaced as a visible assumption for the PM to review, correct, or accept. Outputs are grounded in your PM knowledge base, which is built from documents and interviews you have provided — not inferred from general training data.
Yes. We actively encourage a structured pilot before a full contract. A typical pilot runs 4–6 weeks: onboard one or two PMs, run one complete project through the full pipeline (Discover → Build → Code Forge), and measure the output quality and time-to-spec against your baseline. We will work with you to define success metrics upfront so the pilot has a clear, objective outcome. Contact us to discuss scope and timeline.
Have a question not covered here? Our team is happy to answer security questionnaires, provide architecture documentation, or discuss your specific enterprise requirements.
Contact UsBuild a PM knowledge base once. Every spec after is engineering-ready. Every sprint after ships the right thing. Your knowledge drives the entire pipeline — from discovery to deployed code.
Questions? Reach us at askthili@thili.ai