DEEP MIST
AI

How It Works

From problem to production
in days.

A disciplined process that turns ambiguous problems into running software - no hand-waving, no 18-month roadmaps.

01

Scope

Hours

One focused discovery call. We define the problem, map the solution, and lock the spec. No generic "AI strategy" decks - we come with specific questions.

DELIVERABLEProblem definition document + recommended approach
02

Build

1–10 days

Our AI-powered development pipeline builds, reviews, and tests in parallel. You choose the speed: Priority (24 hours) to Classic (10 days). Triple-model code review and browser QA at every breakpoint. You review and approve before we deploy.

DELIVERABLEProduction-ready software, tested against real data
03

Launch & Support

Ongoing

We deploy to your environment, run final checks, and hand over everything - source code, documentation, runbooks. 30 to 90 days of post-launch support included depending on your tier.

DELIVERABLEProduction deployment + documentation + support period

The old way vs. the Deep Mist way.

Most AI projects fail not because the technology is hard, but because the process is wrong.

Stage
The Old Way
The Deep Mist Way
Discovery
4–6 week vendor evaluation
1–2 day structured call
Design
3-month architecture phase
2–5 day design sprint
Delivery
6–18 month timeline
1–10 day production
Results
Slide decks and recommendations
Working software in production
Risk
Budget spent before code ships
Working prototype before full commitment
Ownership
Vendor-locked
Full source code ownership

Technology we work with.

We choose the right tools for each problem - not the trendy ones.

Language Models
GPT-5.4, Claude Opus/Sonnet 4.6, Gemini 3.1 Pro - matched to use case
Orchestration
Custom multi-agent pipelines, AI-powered code generation and review - built for speed and reliability
Infrastructure
AWS, GCP, Azure - whichever your team already uses
Databases
Postgres + pgvector, Pinecone, Qdrant, Weaviate for vector search
Deployment
Docker, Kubernetes, serverless - matched to scale and team capability
Monitoring
Custom dashboards + existing observability stack integration

Stack current as of March 2026. We evaluate and adopt new models within days of release.

Ready to deploy AI that actually works?

Tell us what you're trying to achieve. We'll tell you honestly if and how AI can get you there.