Two proprietary capabilities, in order: it learns your field, then it maps the path to your goal. The second only impresses once the first lands.
Every system you own remembers your business. Crift is the one that understands it: point it at any domain you run (deals, cases, projects) and it decomposes the thing into what's driving it, what's quietly putting it at risk, and where to act, grounded in your own data, not a generic model's guesswork.
Before a single question is asked, Crift builds itself into a genuine expert on your field, through three pipelines that run in sequence and hand off to each other.
The engine researches the domain on its own and surfaces what matters: the moving parts, the forces, the failure modes a practitioner knows are in play. It doesn't wait to be fed documents; it finds the shape of the field, then enriches each finding into a defined, scored piece of knowledge.
It discovers how those pieces relate, including the non-obvious links an expert sees and an outsider misses. This is the step that turns a pile of facts into a knowledge graph: a web where everything that touches everything else is wired together.
Finally it organizes the learned field so the exact right knowledge surfaces at the exact right moment, when a real problem comes in. Crift calls each living model of a field a namespace, and it shows up to your problem already expert.
Hand it a goal and it returns a full, auditable map of the way through: every step traceable to what it learned, every gap named as its own item you can act on.
The spine: the route from where you are to the goal. The word means a path in Old English; the product is named for what it does.
The forces working in your favor: the things that move the path forward.
The obstacles, the contradictions, the risks working against you. Crift surfaces these instead of hiding them, and names every gap as its own item you can act on.
The unshaped objective enters, however it arrives.
A deal memo, a matter, a goal stated in a sentence. No schema required up front: Crift takes ambiguity as its input, not its obstacle. app.intake → app.project
The engine resolves the objective into a Lode tree.
Probabilistic decomposition descends the problem seam by seam, breadth-first through bands (generate, critique, select), branching into nodes a human can reason about. 8 node types · 6 operator families
Every node is tied to evidence before it stands.
The knowledge graph and RAG substrate anchor each conclusion to source. Nothing in the tree is asserted without a trace back to where it came from. kg.* · rag.* grounding
You survey the structure and stamp it.
The dry-run gate puts the entire proposed tree in front of you before anything commits. You approve, revise, or reject. The machine never moves without your seal. dry_run → ratify
The ratified tree re-projects as a sequenced workplan.
What you approved becomes an executable playbook with owners, order and dependencies, without re-authoring a thing. Reasoning and execution share one source of truth. playbook projection
Crift wears two faces over one core: a configurable system of record your business runs on, and a reasoning engine that maps the way through any problem inside it. Same foundation, same governance, same data.
Through a three-tier ingest. Using advanced models and proprietary knowledge-acquisition techniques, Crift first generates a practitioner-grade corpus of the field, then connects it into a knowledge graph, and organizes it for recall, assembling an expert before the first question in a fraction of the time a person would take. Your own company data folds cleanly into the same corpus.
The reasoning map: a spine of propositions (the Lode), with Crests (helping forces) and Rifts (risks) along it, each node grounded in evidence and ratified before anything commits.