One AI use case, live in production, in weeks
AI sprint
Pick one AI workflow that matters. A lead architect and one or two AI engineers build it, ship it to production and hand it over with docs. Fixed cost, one KPI agreed before we start, 4 to 8 weeks.SaaS teams use it as a paid proof of concept before a bigger build. Founders use it to add their first AI feature. Ecommerce brands use it for cart recovery or support. If it works, it usually grows into a Dedicated team.
Our approach
AI demos are easy. Getting one AI use case live in production is hard. An AI sprint gets you there in 4 to 8 weeks, with one number to judge it by.
An AI sprint is a time-boxed build of one AI use case. You pick the workflow. We agree one KPI and a fixed cost before work starts. A small senior team ships the system into your stack, live with real users, then hands it to your team with docs. No hiring, no managing contractors, no open-ended retainer.
Every sprint is led by an architect who has shipped LLM systems to production. AI coding agents do the repetitive scaffolding and test writing. Engineers own every decision and every line that reaches your main branch.
Here's what backs every engagement
Lead architect (1 per sprint): owns scope, design, the KPI, and the handoff
AI engineers (1 to 2 per sprint): build the agents, RAG pipelines, integrations, and evals
AI coding agents under engineer supervision for scaffolding, tests, and eval harnesses
Specialists pulled in when needed: voice infrastructure, data engineering, security review
Your repos, your cloud, your model accounts. The team works inside your environment
Fixed cost and fixed end date, both agreed in week 1
Before anyone touches your codebase, the use case is scoped and the KPI is written down. The team that builds it is the team that hands it off. When the sprint ends, your team can run the system without us.
AI sprint at a glance
Most AI work dies between the demo and the deployment. A prototype looks great in a notebook. Then it stalls on data access, evals, latency, cost, or an integration nobody scoped. An AI sprint is built for that gap. One use case, one KPI, one senior team, one fixed timeline.
How it compares. A Fixed-price project builds a whole product. A sprint builds one AI workflow, and the KPI is agreed before the first commit. A Dedicated team runs month to month. A sprint ends. If the KPI lands, most clients roll the sprint into a Dedicated team to expand it. What we see every week:
MIT NANDA, 2025
found that most enterprise generative AI pilots produce no measurable P&L impact. The gap comes from integration and workflow fit, not model quality.
DORA research
shows AI lifts individual output, but only teams with strong delivery practice turn that into faster, safer releases.
Our delivery record
is 30+ AI products shipped to production since 2023 in 4 to 8 week cycles, with the KPI set before the build starts.
With great AI comes great responsibility, and TechEmulsion takes that responsibility seriously.
Why it's different
What Makes an AI Sprint Different
01
One KPI, agreed before we build
Week 1 ends with a number: tickets deflected, carts recovered, hours saved per report, calls answered after hours. No agreed target, no build.
02
Production is the finish line
A sprint is not done at the demo. It is done when real users are on the system, with auth, monitoring, evals, and fallback paths in place.
03
Senior-led, agent-assisted
The architect who scopes the work builds it. AI coding agents handle scaffolding, tests, and eval runs under supervision. That is where 4 to 8 week timelines come from.
04
Fixed cost, fixed end date
One use case, one cost, one calendar. No hourly billing, no scope drift, no surprise invoices.
05
Honest results, no spin
If the KPI is missed, we say so and show the data. You see the eval runs and the production numbers, not a slide that talks around them.
06
Handoff is part of the work
Docs, runbooks, eval suite, and a recorded walkthrough go to your team in the last week. You can run it, change it, or hand it to a Dedicated team to grow it.
Across the SDLC
How a Sprint Runs, Week by Week
From discovery and architecture through development, integration, and optimization:
01
Scope and data access (week 1)
- Pick the highest-value use case, not the most interesting one, and write down the KPI and how we measure it.
- Get access to the data, systems, and accounts the build depends on. Nothing is promised before this is checked.
- Deliverables: use case brief, KPI definition, baseline measurement, fixed cost, and end date.
02
Build and demo (weeks 2 to 5)
- Design and build the system inside your environment, on your stack where one exists.
- Eval harness goes in early so quality is measured on real data, not guessed.
- Weekly demo on real data. You see progress, not status reports.
03
Hardening
- Auth, rate limits, logging, and monitoring wired in. Cost and latency measured under load.
- Fallback paths for when the model fails, times out, or gives a low-confidence answer.
- Prompt injection and data leakage checks on anything that touches customer data.
04
Eval
- Run the full eval suite against the agreed KPI and a held-out test set.
- Compare results to the week 1 baseline. Fix the gaps that matter, document the ones that do not.
- Sign-off on the numbers together before anything goes to real users.
05
Launch
- Controlled rollout to real users with monitoring and a kill switch.
- Watch the KPI, failures, and cost in production for the first days.
- Adjust prompts, retrieval, and thresholds based on live traffic.
06
Handoff
- KPI report with production data, good or bad, and what we would do next.
- Docs, runbooks, eval suite, and a recorded walkthrough for your team.
- Code, infra, and model accounts confirmed in your name. Next step scoped if you want to keep going.
Client outcomes
What a Sprint Changes
Every sprint ends with a number and a handoff. AVL Copilot (RAG for AV integrators), Pack Assist (RAG sales chatbot, shipped in 8 weeks), The Meatery (voice AI CRM), and Conversa (voice cart recovery) all started as one use case with one metric.
| Task | Before | After | Impact |
|---|---|---|---|
| Ship a RAG chatbot for product support | Internal team learns RAG, evals, and vector search while shipping. Months of trial and error, no clear finish | Sprint scoped in week 1, live with real users by week 8, evals and docs handed over | A finish date and a KPI instead of an open-ended experiment |
| Add a voice agent for missed and after-hours calls | Team evaluates telephony, speech models, and CRM sync on the side of their day jobs | Voice stack, CRM sync, and fallback to a human wired in one sprint (The Meatery, Conversa) | One team owns the whole path from call to CRM record |
| Prove an AI feature before funding a full build | Free pilot with no owner and no metric, drifts for a quarter | Paid proof of concept with one KPI, a baseline, and a production launch | A go or no-go decision backed by production data |
| Take a stalled prototype to production | Lovable or notebook demo that works on stage and breaks on real data | Audit in week 1, rebuilt on a production stack with auth, monitoring, and evals | Real users on the system instead of a demo nobody trusts |
The pattern is the same each time. Pick one leak, fix it, measure it, then decide whether to expand it. That is how AI pays for itself.
Tools & platforms
What Sprints Build With
Sprints build on Claude and other frontier models, on a stack we have shipped 30+ times. If you already have a platform, we build on it. You own the code, the infra, and the model accounts.
- Claude API and OpenAI models
- RAG with pgvector or Pinecone, LangChain or LlamaIndex
- FastAPI for model services and evals
- Supabase for auth, data, and storage
- n8n for workflow glue and triggers
- Next.js on Vercel, Twilio and Vapi for voice
Why TechEmulsion
Why Teams Choose an AI Sprint
30+
AI products shipped to production since 2023
4 to 8 wks
Scope to handoff for one use case
1 KPI
Agreed and baselined in week 1
Fixed
Cost and end date, both set before the build
Senior-led
Every sprint run by an architect who has shipped LLM systems
Yours
Code, infra, evals, and model accounts stay in your name