Praxis
From business goal
to proven result.
Praxis turns business requirements into working, tested data solutions: pipelines, dashboards and reports. AI does much of the work. Your team makes the important decisions, and every step is recorded.
The run
One run, every step recorded.
Every step is saved as it happens, so you can see what was done, by whom and why. Built for large companies, and useful for smaller teams too.
01
Outcome
Objective
02
Understanding
Context
03
Work
Progress
04
Attention
Human decision
05
Result
Conclusion
06
Evidence
Proof
01
The case
The enterprise gap
The enterprise gap
The missing layer between ambition and production.
Organizations rarely fail because nobody can write code or call a model. They lose time and confidence in the translation between the business outcome, the rules and data that shape it, the system that gets built, and the evidence required to accept it. Praxis turns that translation into one observable, governed run.
Outcomes, not tickets
Move from the result
Begin with the business result that needs to exist. Praxis turns that objective into structured work and keeps the original intent visible as the implementation evolves.
Escalate judgment
Not routine work
Praxis performs technical and analytical work where it can, while surfacing the questions, approvals, conflicts and authority transitions that genuinely require people.
Prove the result
Work is not outcome
Work performed, outcome achieved and evidence are kept distinct. Inspect what changed, why, what was verified, and what remains uncertain.
02
How it works
How Praxis works · What Praxis does
How Praxis works
Tell Praxis the outcome. Stay accountable for what matters.
Tell Praxis the business outcome you need. Give it the policies, data, systems, documents and context that matter. Answer the consequential questions that require human judgment. Let Praxis perform the work, then inspect the result and the evidence behind it.
Complexity is accessible, not imposed.
What every run records
Illustrative
Objective
The business outcome Praxis is working toward.
Progress
What is complete, current, pending or blocked.
Decisions
Consequential questions, approvals, authority and rationale.
Result
What the system can currently conclude about the outcome.
Evidence
The observations and artifacts that support or limit that conclusion.
What Praxis does
One system. Four business activities.
01 · Intent
Specify
Specify
Intent and authority into specification
Work from objectives, policies, regulations, standards, source documents and stakeholder input to clarify what the outcome requires and where authority comes from.
What you get ↓
- Requirements traceable to their source
- Business-readable specifications and BRDs
- Explicit constraints and acceptance conditions
- Open questions, rather than fabricated certainty
02 · Meaning
Understand
Understand
Make business meaning explicit
Connect business concepts to the data, models, code, tests, lineage, terminology and existing implementation that give them operational meaning.
What you get ↓
- Semantic and data models
- Ontology and terminology mappings
- Brownfield understanding of an existing estate
- Reusable semantic contracts for delivery and verification
03 · Change
Deliver
Deliver
Move the approved objective into working assets
Plan and perform implementation through governed actions, reconcile what actually happened, and preserve the evidence required to understand the resulting state.
What you get ↓
- Data pipelines, SQL and deployment bundles
- Analytics assets, dashboards, configurations
- Reconciliation of intended against actual state
- Implementation artifacts with provenance
04 · Proof
Verify
Verify
Authorized expectations into executable proof
Derive what must be true, distinguish necessary expectations from plausible ones that need human judgment, and compile authorized expectations into deterministic checks and evidence.
What you get ↓
- Semantic expectations
- Executable tests and checks
- Acceptance evidence and limitation traces
- Stronger release confidence
03
Built for trust
The difference · Trust · Databricks · Customization
The difference
Not another agent that stops at “done.”
Most AI delivery tools optimize one part of the chain: planning, coding, orchestration, testing or chat. Praxis is designed around the whole relationship between intent, authority, meaning, implementation, verification and evidence. Its job is not to produce activity. It is to help you reach an outcome you can understand and defend.
01
Outcome-centered
The primary object is the business outcome and the run working toward it, not the agent, ticket queue or chat transcript.
02
Authority-aware
A model proposal is not permission. Consequential decisions and approvals stay explicit, attributable and human-owned.
03
Evidence-native
Generated artifacts, successful commands, passing checks and accepted outcomes are not collapsed into one claim. Each stays inspectable.
04
Brownfield-capable
Works with your existing estate (current code, tests, docs, models and platform state), not only greenfield.
05
Model and host flexible
No dependency on owning the chat interface or a single model provider. Product state and authority stay durable outside the conversation.
06
Composable, accountable
Specialized responsibilities stay distinct under the hood. Coordination never erases who owns specification, semantics, delivery or verification.
Trust
Automation where it helps. Human judgment where it matters.
When a run hits a consequential ambiguity, approval, authority transition, conflict, stale decision or missing evidence, it surfaces that condition directly instead of quietly inventing an answer. Praxis can propose resolutions, but proposals become authoritative only through the owning workflow’s reviewed transition.
Attribution
Decisions keep their owner
Human decisions retain attribution and rationale. Stale reviews are invalidated when relevant state changes.
Honest uncertainty
A gap is shown as a gap
Missing or unavailable evidence is shown as a limitation, not converted into confidence. Read-only is distinct from mutation.
No false proof
A command is not an outcome
A successful technical command is not treated as proof the business outcome was satisfied. Technical users can inspect the exact records.
For regulated organizations, Praxis preserves the chain from authority to requirement to implementation to verification. For everyone else, the same discipline simply means fewer expensive misunderstandings.
Databricks
Built to make governed platforms more productive.
Databricks is Praxis’s first and deepest platform integration. Praxis complements those controls rather than recreating them: Databricks stays the enforcement point for platform identity and permissions; Praxis adds the business-engineering layer connecting approved intent to implementation, human decisions, execution evidence, reconciliation and acceptance. Need the Databricks platform itself built and run? That is Tekton, our platform engineering system.
01
Better together
Databricks-native outcomes
Move from approved business intent to Databricks-native outcomes (Unity Catalog estate understanding, Asset Bundles, pipelines, SQL and AI/BI assets) and retain the evidence that what was delivered is what the business authorized.
02
Not a replacement
Enforcement stays native
Praxis does not replace Unity Catalog, Databricks IAM or platform deployment. Platform identity, permissions and governance remain Databricks responsibilities.
03
Platform-independent
First, not only
Product contracts are separated from ecosystem operations, so additional platforms can be supported without rewriting the core model. Databricks is the first implemented ecosystem, not a constraint.
Customization
Make Praxis work the way your organization works.
Every organization has its own standards, terminology, patterns, practices, templates, controls and approved tools. Praxis incorporates that operating context without forcing you to fork the product or give custom instructions unrestricted authority. Extend it with versioned, governed contributions: terminology, delivery practices, templates, platform conventions, bounded command tools and domain context. All of it stays subordinate to Praxis’s product contracts and authority boundaries.
Customize the operating context, not the accountability model.
04
Getting started
Use cases · Under the hood · Start
Where Praxis is useful
Start where translation, coordination or proof is expensive.
Regulated change
Data and reporting change
A policy, regulation or control changes. Praxis interprets the authority, turns it into requirements, maps meaning into the data estate, implements the change and preserves verification evidence.
Data products
Analytics delivery
A leader needs a new outcome, but requirements, definitions, sources and acceptance criteria are incomplete. Praxis turns the request into an understood, implemented, verified product.
Brownfield
Modernization
Existing pipelines, SQL, notebooks, tests, models and years of decisions. Praxis establishes what exists, recovers business meaning and guides a safer path.
Quality and evidence
Acceptance
“Good” is trapped in prose, tribal knowledge or inconsistent tests. Praxis turns authorized expectations into repeatable checks and evidence.
Early-stage
Build it right from the start
Praxis doesn’t require years of governance to be useful. Establish requirements, terminology, acceptance and disciplined decision points from the beginning.
Under the hood
Specialized systems. One Praxis experience.
Praxis is composed from independently useful products with explicit responsibilities. You don’t need to know them to use Praxis. They’re why its boundaries and accountability hold up.
System
Activity
Responsibility
Nomos
Specify
Turns authority and approved business intent into governed requirements and specifications.
Logos
Understand
Models business meaning across data, terminology, code, tests, lineage and the existing estate.
Telos
Deliver
Governs delivery, actions, reconciliation, release and acceptance.
Overproof
Verify
Derives assurance expectations and compiles authorized expectations into executable verification and evidence.
Overmesh
Coordination
Coordinates runs, artifacts, versions, dependencies, handoffs, freshness and execution identity, without taking over domain authority.
Coordination does not erase responsibility. Each specialized product continues to own its domain truth.
Start
Start with one result that matters.
The best first Praxis engagement is not a platform-wide transformation. It is one business result with enough value to matter, enough access to be feasible, and a clear way to prove it was achieved.
01
Frame
The outcome
What should be true for the business when the work is complete?
02
Name
The proof
What evidence would convince stakeholders the outcome was achieved?
03
Expose
The constraints
What policies, data, systems, owners, approvals and controls govern the work?
04
Run
And learn
Run Praxis on the real problem; measure speed, quality, traceability and reuse.
FAQ
Questions enterprise buyers ask.
Is Praxis an AI agent?
Praxis uses AI reasoning where it is valuable, but describing it as a single agent misses the point. It is a Business Engineering System with durable product state, explicit authority boundaries, specialized responsibilities, governed actions and inspectable evidence. The model helps reason; it is not the source of organizational authority.
Does Praxis make decisions on behalf of the business?
Praxis resolves routine technical work and proposes answers where reasoning helps, but consequential decisions, approvals and authority transitions stay explicit. It surfaces the moments where human judgment matters rather than hiding them inside an autonomous workflow.
Does Praxis replace Databricks?
No. Praxis makes governed platforms more productive. On Databricks, identity, permissions, data governance, compute and deployment remain Databricks responsibilities. Praxis connects intent, requirements, semantics, delivery, verification, decisions and evidence around that governed surface.
Is Praxis only for regulated industries?
No. Regulated organizations make the value of traceability and authority especially obvious, but the underlying problem is universal: expensive misunderstandings between business intent and technical delivery. Earlier-stage teams use the same discipline to avoid creating those gaps.
Can Praxis work with an existing environment?
Yes. Praxis is designed for brownfield as well as greenfield. Existing code, data models, SQL, tests, documentation and platform metadata contribute to its understanding of the current estate.
Does Praxis require a specific model provider?
No. Praxis is built around a provider-neutral reasoning boundary and does not need to own the conversation interface. It operates with an approved host while product state and governance stay outside the model session.
How should we evaluate Praxis?
Bring a real business problem, the relevant context and the people who can answer consequential questions. Define in advance what evidence would count as success. Then evaluate the run on speed, quality, stakeholder effort, traceability, verification and how much of the work is reusable.
Book a demo
See Praxis deliver a real result.
Bring us one business problem. We’ll show you how Praxis would handle it, not a generic AI presentation.