Building AI-native systems is not building traditional software. Requirements are probabilistic, behaviour is emergent, and quality must be measured continuously — not signed off once. ADLC is Contemplr's answer: a disciplined loop with observability and evaluation woven through every stage.
Deep discovery. Observe the business, surface the real problem, define what "good" means — measurably.
Data & solution architecture with observability designed in. Choose models, guardrails, and eval strategy up front.
Iterative engineering of models, pipelines, and apps. Code review, test coverage, and documentation are non-negotiable.
ClickHouse-backed telemetry, model evaluation, and quality gates. Nothing reaches production uninstrumented.
DevSecOps go-live — provisioning, release management, cutover, and hypercare. Never a cliff edge.
Observed behaviour feeds the next loop — tuning models, expanding coverage, compounding value over time.
In ADLC, observability isn't a phase — it's the connective tissue. Every stage emits signal, and that signal continuously informs the next. The loop never truly closes; it compounds.
Traditional SDLC assumes fixed requirements and pass/fail tests. AI behaviour is probabilistic, so ADLC replaces one-time sign-off with continuous evaluation.
Software is "done" at release; AI systems drift. ADLC treats go-live as the start of an evolution loop driven by observed real-world behaviour.
Logs tell you what happened. ADLC instruments models and outcomes so you understand why — and can reason about what to change.
Well-scoped initiatives with clear outcomes. We define scope, agree milestones, and deliver with full accountability — no overruns without sign-off.
Monthly retainer access to architects, engineers, and advisors as an extension of your team. Ideal for ongoing AI, observability, and data work.
Need ML engineers, data architects, ClickHouse specialists, or LLM developers? We embed the right people from our global pool into your workflows.