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). Flagship-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 make sure everything works under real conditions.

DELIVERABLEDeployed system running in your environment

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
Issues
Vendor-locked
Optimized for your environment

Traditional AI projects take weeks for vendor evaluation, months for architecture, and 6–18 months for delivery — often producing slide decks instead of working software. Deep Mist AI compresses this to a 1–2 day discovery call, a 2–5 day design sprint, and 1–10 day production delivery of deployed, working software.

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.

Our Review Process

Three models. Zero blind spots.

Every line of code is reviewed by three flagship AI models before a human engineer signs off.

GPT-5.4Claude 4.6Gemini 3.1 Pro

FAQ

Common questions

What happens during the discovery session?

A focused 1–2 hour call where we define the problem, map the solution architecture, and lock the technical spec. We come with specific questions — no generic AI strategy workshops.

Can I review work in progress?

Yes. You review and approve before we deploy. During build, our pipeline runs in parallel — building, reviewing, and testing simultaneously. You get visibility at key checkpoints.

What about post-launch support?

Every engagement includes post-launch support. Foundation includes 30 days, Growth 60 days, Enterprise 90 days. After that, we offer ongoing retainer arrangements.

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.