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Pipeline architecture, dependency model, citation provenance, and integration surface.
It is a property of the data layer beneath, not an orchestration trick. The R&D Workbench does not retrieve at run time. The corpus is already structured, correlated, and queryable in ColossioDB: graph, vector, temporal, geospatial unified at 200+ petabyte scale. Atomic queries return net unique relevant content in milliseconds. Twelve stages run in sequence because each query is fast enough that the next stage can act on its output immediately.
Strict directed-acyclic ordering. Each stage consumes the artefacts of the stage above it as typed inputs and produces typed outputs that downstream stages declare as prerequisites. The pipeline is not a free-form agent loop; it is a deterministic execution graph. A stage cannot start until its declared inputs exist and have passed schema validation. This is also why any stage can be re-run from its inputs without disturbing stages above it.
Every artefact at every stage carries citation links to the source spans in ColossioDB that supported each claim. When Stage 3 validates a Stage 2 chapter, the validation report cites the corpus spans that confirmed or contradicted each claim. When Stage 6 drafts a patent from a Stage 3 validated chapter, the patent's claims trace through to the validating evidence and through that to the original sources. The full archive is a directed graph of citations, traversable from any artefact.
Fully model-agnostic. The pipeline orchestrates LLM calls but does not depend on a specific model. Different stages can use different models if that improves cost or quality. Because each stage receives a dense, focused payload from ColossioDB rather than raw context, compact open-weight models like gpt-oss-20b often produce comprehensive results, including in-house or private-cloud deployments where data sovereignty matters. Frontier-scale models are used where their reasoning depth is genuinely needed.
Yes. Each stage exposes a typed API contract: inputs, outputs, schema, citation provenance. The full pipeline is one orchestration over those APIs; teams can call individual stages directly for narrower workflows or compose custom sequences. Existing agentic frameworks (LangGraph, AutoGen, CrewAI) can invoke stages as tools, gaining a foundation that fundamentally changes what they can accomplish.
What you get out of a thirty-minute run, and how it differs from asking Catalyst directly.
Catalyst answers questions you ask it. The R&D Workbench takes a research idea written in plain English and, in about half an hour, turns it into a complete project package: the problem framed, a solution proposed, the solution checked against evidence, a plan to build it, the equipment needed, the patents drafted, the people to hire, and a focused 80/20 plan. Twelve outputs, one sitting. It's the difference between asking a librarian a question and asking a research team to scope a programme.
Because the hard part, having access to the world's relevant content and being able to search it instantly, is already done. The Workbench sits on ColossioDB, an architecture that already holds 200+ petabytes of organised, connected research content. So instead of going off to search and gather and check, it asks the corpus precise questions and gets answers in milliseconds.
A complete project archive: twelve linked artefacts plus optional visualisations. Read the executive summary, drill into any stage, follow citations back to source, or pull any individual artefact (the roadmap, the patent drafts, the recruitment posts) for direct use. Everything is traceable, so anyone reviewing it can audit how every claim was reached. These are deliverables, not summaries.
Every claim, at every stage, has citations linking back to the source content in ColossioDB. Stage 3 (Independent Validation) explicitly re-checks the proposed solution against external evidence and scores how well-supported each part is. Where evidence is thin or speculative, the Workbench tells you. It doesn't fabricate confidence.
Yes. Each stage can be invoked individually: say, a patent investigation on an existing draft, or a use-case portfolio for a solution you've already designed. And because a full run takes thirty minutes, the cost of asking "is this idea worth pursuing?" is low enough that you can ask it routinely. Run the Workbench on three or four candidate ideas and compare the archives.
The commercial case, the people impact, and the comparison to consultancy spend.
Catalyst answers questions. The Workbench answers programmes. From a single free-text problem statement it produces twelve linked artefacts in under thirty minutes. The work that today takes a research team, a strategy team, an engineering function, an IP firm, and a head of talent running for weeks happens in one pass. Programmes that previously couldn't justify the upfront cost of definition now can. You evaluate more ideas, kill weak ones earlier, and arrive at funding decisions with deeper evidence behind them.
Time saved on programme definition is measurable, typically tens of person-weeks per run. Avoided dead-ends are measurable in retrospect. But the real value is opportunity-side: the breakthroughs you find because the corpus is fully in scope, the partnerships you spot because the use-case stage discovered them, the IP positions you defend because the prior-art stage caught the issue early. Cost ratio versus commissioning a strategy consultancy or research institute for the same outputs is typically two to three orders of magnitude lower per programme run.
Direct users tend to be principal investigators, programme leads, R&D directors, and innovation managers. Downstream beneficiaries are broader. The change isn't headcount; it's where senior R&D talent spends its time. Your scientists, engineers, and strategy teams shift from producing the artefacts to interpreting them and making strategic decisions on top of them.
Two to three orders of magnitude lower cost per programme run, with turnaround in days rather than quarters. Beyond economics, there's a coverage advantage: the Workbench reasons over the full global corpus on every run. Outputs are reproducible and auditable; any stage can be re-run from its declared inputs. Many customers continue to use external advisors for specific judgement-heavy decisions, but the Workbench replaces the bulk of artefact-production work.
Asymmetry, and it compounds. A competitor that can define, validate, and IP-protect a research programme in a day is operating on a fundamentally different decision cycle. They evaluate more candidate programmes, abandon weak ones earlier, and commit to strong ones with deeper evidence behind them.