Foundation
Launch of the d8n.ai platform with core BPM modules. The most critical stage — the right foundation — so we lead it.
The DATM methodology
DATM (Documentolog AI Transformation Methodology) is our structured path from “document flow in email and Excel” to “AI agents do a meaningful share of the work”. It’s not a software rollout but an organizational transformation: diagnostics, five stages and three parallel streams.
Most companies try to “buy AI” and launch an agent right away. An agent on un-redesigned processes and an empty knowledge base gives generic, useless answers — running at a third of its potential. That’s the pit most AI projects fall into.
DATM reverses the order: foundation first — formalized processes, clean data, a structured knowledge base and governance — and only then agents inside the processes. Every stage ends with a measurable result you sign off on, not an endless pilot.
Why joint delivery works
67% vs 22%
Per MIT, where external expertise works alongside the client’s internal team, 67% of projects reach production — versus 22% for those building with internal IT alone. That 45-point gap is what a governed methodology buys you instead of trial and error.
Discovery & Assessment
Discovery & Assessment2–4 weeks
Before any major commitment we assess readiness together, pick candidate processes, build the business case and design the route. It’s a paid entry that is credited toward the rollout cost — and the first point where the real scope becomes visible.
- AI-readiness assessment across 8 dimensions
- Prioritized catalog of candidate processes
- Business case with ROI and payback
- Process pain-point map
- Roadmap and a scoped statement of work
Five rollout stages
Stage 5 (agents) cannot start without stages 1–4. Foundation first, intelligence second.
Process Adaptation
Redesigning processes around agents: “as-is” → “to-be”. We don’t bolt AI onto legacy — we rebuild the process.
Knowledge Base
Building a structured knowledge base and document catalog — without which agents hallucinate. The most underrated stage: the difficulty is organizational, not technical.
Agent Configuration
Indexing the base, configuring RAG, access rights and calibrating agents to your real data.
Agents Rollout
Wave-based agent deployment with a pilot and a measurable business result. Prove the value, then scale by function.
The core principle
Launching an agent on un-redesigned processes and an empty knowledge base yields generic, useless answers. That’s why agents are the fifth stage, not the first. The hardest step (stage 3) is organizational, not technical: getting the team to write structured documentation. We guide you through it methodically rather than leaving it for later.
Three parallel streams
They run through all five stages — because 70% of AI’s value comes from people and processes, not the model.
Change Management
Engaging people, communications, training, an AI-champions program and handling resistance. Without formal change management, most rollouts miss their target ROI due to low adoption.
Governance & Risk
AI governance charter, Responsible AI ethics, a risk register and compliance with Kazakhstan law (Personal Data Act No. 94-V).
Data Strategy
Integrating raw data from CRM, ERP and HR systems, access controls and a retrieval architecture for the agents.
Why DATM is “all in one”
The Big Four sell methodology and hands but without their own platform. Cloud vendors give a platform and a handbook but no hands. We’re the only ones combining all of it:
Platform
Our own AI-native BPM platform where agents work inside processes at the software level.
Methodology
DATM with 25 ready templates in 5 categories — from an AI-readiness questionnaire to an ROI measurement framework.
Hands
An implementation team that delivers with you, instead of leaving you with a handbook.
Kazakhstan context
Data sovereignty, e-signatures, language and Kazakhstan regulation — out of the box.
We ran DATM on ourselves
Documentolog is the first client of its own methodology. We went through the diagnostics, assessed our maturity, selected candidate processes and are building AI agents inside the company as a reference case. It’s “build in public”: for every toolkit template we have our own completed example.
Start with diagnostics
Discovery reveals your AI-readiness, candidate processes and ROI before any major commitment — and is credited toward the rollout cost.