Most AI projects fail not because the technology doesn't work, but because the team jumps to model selection before they understand the problem. Our process is structured to prevent that. Three phases, clear deliverables at each, and a working system in your hands before we ever scope phase two.
Phase 01 · Understand & Design WEEK 1–2
What we do: Before any model is chosen or any code is written, we sit with your team and audit the ground truth. What data do you actually have? Where does it live? What is the workflow this AI is replacing or augmenting? What does "success" look like in measurable terms? What are the compliance constraints — GDPR, HIPAA, DPDP, industry-specific?
Deliverables:
- Data audit report — what you have, what's clean, what needs work
- Model and architecture proposal — which approach, which base model, why
- Success metrics — the numbers we'll hit to call this a win
- Compliance posture document — what we can and can't do with your data
- Budget and timeline for Phase 02, fixed not hourly
Phase 02 · Train & Build WEEK 3–6
What we do: Fine-tuning runs, pipeline orchestration, UI development, integration points. Everything is version-controlled from day one. Every training run is justified by an eval metric. We share a weekly demo with a working artifact — a model checkpoint, a workflow running on test data, a chat UI you can click through. No surprises at the end.
Deliverables:
- Fine-tuned model checkpoint or RAG pipeline, reproducible end-to-end
- Working application UI (chat, dashboard, or embedded widget)
- Evaluation harness proving the model beats the baseline
- Integration with at least one real data source
- Weekly demos in a staging environment
Phase 03 · Deploy & Scale WEEK 7+
What we do: Stand up production infrastructure on your chosen target (on-prem, private cloud, hybrid). Hand over the keys with full documentation. Monitor the system, catch drift, retrain as your data evolves, and scale alongside your team as you grow. We stay engaged on a retainer or transition fully to your engineers — your call.
Deliverables:
- Production deployment on your infrastructure of choice
- Terraform / Ansible / Docker Compose — full infra-as-code
- Observability dashboards (Grafana, token usage, latency, error rates)
- Runbook for common incidents and model updates
- Knowledge transfer session with your engineering team
- 30-day post-deployment support included
What this looks like from your side
You'll have one point of contact from Adorbis from the first call to the final handover. We work in 1-week sprints with a shared Notion or Linear board so you see every task, every blocker, every deliverable. Weekly syncs are short — 30 minutes — and they produce decisions, not status reports.
What we don't do
- We don't start with a vague "AI strategy" engagement that produces a deck and no software
- We don't bill hourly for indefinite scope — every phase is fixed-price and time-boxed
- We don't build proof-of-concepts that never reach production — Phase 02 ships something real
- We don't lock you into our tools — everything runs on open standards and open weights