One three-phase process to decide, build and sustain AI in your company, and three ways to start depending on your operation's starting point. Every delivery is handed off and in production, not a pilot that depends on us.
First we decide well, then we build and hand off, and finally we sustain the asset in production. Here's what each phase looks like: what happens, what we deliver and what stays installed in your team.
Phase 1
What happens: we understand the operation before touching it — we map processes, data and stack.
What we deliver: cases prioritized by value, feasibility and risk, a measurable baseline and the reference architecture.
What stays in the company: your own roadmap, with a clear starting point and execution order.
Phase 2
What happens: we build on what is already in production, not next to it.
What we deliver: AI scaffolding and harnesses, integration with the existing stack, the team's work surfaces and governance with human approval.
What stays in the company: the system running and handed off to the internal team.
Phase 3
What happens: the asset stays in operation and keeps improving after the project has closed.
What we deliver: self-monitoring, model updates and new capabilities as they appear.
What stays in the company: capacity built into the team to sustain and expand the asset without depending on a vendor.
The same three-phase process is deployed in three different ways, depending on your company's starting point. Choose the one that matches yours, or combine both in a comprehensive deployment.
Nothing goes live without an owner, a metric, and a guardrail.
Business inputs — what the company already has
Data
Operational
CRM, ERP, analytics, ad spend
Input Source map with an owner
External
Public and government data, with extraction and generation methods
Input Qualified external sources
Documentation
Procedures, contracts, history
Input Living knowledge base
Operating judgment
How decisions get made, what never gets done
Input Explicit business rules
Processes underway
Who hands off what to whom
Input Rollout priorities
Technical architecture
Understanding of the IT and data model
Input Technical map of the starting point
Operating judgment doesn't live in any database.
The layers that hold up the asset, from the most structural to the most visible
Deployment-specific customization, owned by the company. It isn't rented, it isn't returned, it doesn't leave with the vendor.
Scaffolding
Context, reusable capabilities, and the structure that holds everything up
Governance
Rules, guardrails, and human approval
Integration with the stack
Connection to the systems already running
Data integration
Connection to in-house and external data sources, to enrich with insights and analysis
Repository
Source of truth where the scaffolding and the source code are stored and versioned.
Continuous monitoring
Agents deployed inside the asset that monitor it autonomously
Hybrid deployment
The harness decides which model handles each task. Changing models doesn't change the asset.
Harness
Selection and deployment of the human-AI operating interface.
Models
Frontier models for maximum capability, open models to control cost and data, in the cloud or on-premise depending on data sensitivity.
What people use from day one. It's the visible deliverable.
Business knowledge
Access to your business's context and know-how, powered by AI
Intelligence agents
AI that extracts value and insights from your data sources: CRM, ERP, databases
Automation
Processes run by agents that evaluate and follow the optimal path
Planning AI-adoption dynamics for the team.
Deployed processes and transformation
These aren't new tools: they're the same processes, made more powerful — each one with an owner, a metric, and a guardrail.
Production processes
The core of the business
Internal operations
Admin, finance, people
Sales cycle
End-to-end acceleration
Service and support
Response and after-sales
Decision-making and reporting
Analytical processes with insights, faster decisions
Research processes
Synthesis with traceable evidence
Creative processes
Fast iteration with brand judgment
Continuity
The asset keeps improving after the consultant is gone, operated by the company itself.
Every process goes in with a baseline and comes out with a comparable number.
D57 connects and governs the systems that already exist. Prior data engineering is a separate scope.
Book a diagnostic and we'll define together which modality your operation should start with.
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