PRODUCTION AI · EST. 2026

Automate the boring, ship the remarkable.

StackV designs and ships production AI systems — custom agents, intelligent workflows, and growth automation — built around how your business actually runs.

Avg. delivery 14 days
Stack LLM · n8n · Edge
Reliability 99.94%
01 / Services

A small team that ships production systems — not slide decks.

Flagship

Custom AI agents that handle real work — sales, support, ops.

Discovery, design, build, and a 30-day runway. We hand you a system, not a prompt.

From €6,400 · 14 days
Web build

Marketing sites & product surfaces that earn the click.

App build

iOS, Android, & web apps — shipped on a fixed runway.

Workflows

n8n & Zapier rebuilds, but actually maintainable.

RAG

Search your knowledge with citations you can trust.

Voice

Inbound voice agents · < 400ms latency.

Growth & strategy

Lead routing, enrichment, and an AI roadmap that doesn't gather dust.

Care plan

Monthly ops & iteration retainer — we stay after launch.

A note from the studio

We don't sell magic. We ship small, ugly, working systems — then make them better, week after week.

~3 wks From kickoff to first thing in production
94% Of engagements still running after 6 months
1–2 Active client builds we take on at a time
Built in Pune · Shipping globally Q3 2026 — two slots open
02 / Process

Four weeks from “we should automate that” to live in production.

Week 01
D

Discover

Map workflows, decisions, and the systems involved. Pick the one with the biggest payoff.

Week 02
P

Prototype

A working slice your team can poke at — real data, real edges, real failure modes.

Week 03
B

Build

Hardened, monitored, version-pinned. Auth, logs, evals, and a kill switch on day one.

Week 04
L

Launch

Rollout, training, dashboards — and a 30-day runway in case anything wobbles.

03 / About

A studio out of Pune, building boring AI that actually ships.

We're builders, not slide-decks. StackV started in 2024 after a decade of doing the same thing inside larger product teams: shipping production AI that survives Monday morning, not just demo day.

Every engagement is small, named, and senior. No project managers translating you to engineers. You talk to the people writing the code, and the system you walk away with is yours — repo, runbooks, and all.

38 Systems shipped

Across ops, sales, support, and growth.

14d Median delivery

From kick-off to first production traffic.

92% Client retention

Clients renew into a care plan after launch.

€0 Locked-in fees

You own the stack. Cancel any time.

L

Lakshya

Co-founder · Systems

Years building production ML at fintechs. Refuses to ship anything without a kill switch.

P

Piyush 

Co-founder · Design

Design lead from two B2B SaaS teams. Cares more about retention than CTR.

T

Tanishq

Head of Growth · Outreach

Ran cold email at two B2B startups. Treats deliverability like a religion, not a setting.

P

Pushpak

Data & Compliance Lead

Built scraping pipelines under strict privacy regimes. Reads data law for fun, blocks shipping without a DPA.

Y

Yash

SDR · Vertical Sales

Closed SMB deals across multiple markets. Speaks operator-fluent, books demos faster than reps draft scripts.

How we work

Three rules we don't break, even on the messy projects.

01 / Boring > clever

If a Postgres table and a cron job will do, we don't reach for an agent.

We use the simplest thing that survives the next on-call. AI is a tool, not the headline.

02 / Ship every week

Something real lands in your account by Friday — every Friday.

No 6-week "discovery" before you see code. Tiny, ugly, working first; pretty and complete after.

03 / Own your stack

The repo, the prompts, the runbook — all yours on day one.

No black boxes, no lock-in, no "call us to make a change." You should be able to fire us cleanly.

04 / Recent work

The folder we open first when a new client asks “have you done this before?”

Atelier Lumière Web build · Lyon

A boutique furniture maker moved off WordPress and finally started selling online.

Rebuilt their site on a headless stack, added a Stripe checkout with VAT-aware shipping, and let the founder publish new collections in under a minute.

Conversion rate
0.9%4.1%
Largest contentful paint
2.8s0.6s
Time to publish a product
~40 min< 1 min
Online revenue (90d)
€0€86k
Next.js Sanity CMS Stripe Vercel
Shipped · Mar 2026 Open folder →
VoltShift Energy Automation · B2B

From a 17-hour lead lag to a same-minute Slack ping the SDR can actually act on.

Enrichment, scoring, and routing at form-submit. Owner gets a one-click “accept / send back” in Slack; rejected leads become training data.

Form → owner assigned
17h4 min
First-hour reply rate
22%51%
SDR triage time / day
2 hrs9 min
Pipeline / month
€184k€312k
n8n Clearbit Apollo HubSpot Slack
Shipped · Feb 2026 Open folder →
Aria Health App build · iOS + Android

A patient companion app that turned reminder fatigue into a 71% adherence rate.

Cross-platform Expo app with personalized check-ins, push notifications, and a quiet GPT-4o-mini coach that drafts the next nudge based on response history.

Medication adherence
38%71%
Daily active users
12%58%
Support tickets / 1k users
9421
Crash-free sessions
96.1%99.8%
Expo React Native Supabase Twilio GPT-4o-mini
Shipped · Jan 2026 Open folder →
NovaRetail AI agent · Support

The Tier-1 support agent that finally stopped escalating customers on turn three.

Plan → act → verify loop with typed tool calls, sentiment-aware escalation, and a 14k-ticket replay harness so every change ships behind a regression score.

Autonomous resolution
38%86%
First response
8 min22 s
CSAT vs human baseline
4.5/54.6/5
Cost per ticket
€3.40€0.41
Claude 3.5 GPT-4o Postgres Slack Sentry
Shipped · Dec 2025 Open folder →
05 / Case studies

Three systems, three different fires. All of them shipped.

Case 01 B2B SaaS · Support 5 weeks · Fixed scope

Why most agent demos don't survive contact with a real inbox.

A support agent that aced demos kept escalating real tickets the moment threads got long, attachments showed up, or a customer got frustrated mid-conversation. Production resolution rate sat at 38%. The team was a month away from killing the project.

● Live in production ~4,200 tickets / week

Where it was failing

The agent ran a single "read email → write reply" loop. The instant a thread had three turns, a PDF, or a passive-aggressive line, it would hallucinate a refund policy or punt with a generic apology — neither of which the support lead would accept.

What we did

  • Replaced the one-shot loop with an explicit plan → act → verify cycle, every tool call typed and retryable.
  • Added a sentiment + complexity classifier on the inbound message, escalating high-risk threads on turn one instead of turn three.
  • Built an offline replay harness from 14k historical tickets so every change ships behind a regression score, not a vibe.

Outcome

86% Autonomous resolution
(was 38%)
22s First response
(was 8 min)
4.6/5 CSAT held flat
vs human baseline
14k Regression fixtures
shipped in the repo
Stack GPT-4o Claude 3.5 Postgres n8n Slack escalation Sentry
Case 02 B2B SaaS · Sales 3 weeks · Fixed scope

How we cut a sales team's lead triage from 2 hours to 9 minutes.

Two SDRs spent the first two hours of every morning enriching, scoring, and routing the previous day's inbound leads. By the time the good ones got assigned, half had already booked a demo with a competitor.

● Live in production ~600 leads / week

What was broken

Lead routing was a Notion checklist. Scoring was a spreadsheet last touched in 2023. The latency between "form submitted" and "owner assigned" averaged 17 hours — most of it overnight, the rest spent on copy-paste.

What we did

  • Enriched every new lead with Clearbit + Apollo at form-submit time, not the next morning.
  • Replaced the rules-based scorer with a small LLM tiebreaker that explains why each lead got its grade — so reps trusted it.
  • Routed assignments to owner Slack DMs with one-click accept or "send back", and logged the rejection reason as training signal.

Outcome

9 min Triage time
(was 2 hours)
+34% Reply rate
first-hour outbound
€0 Added headcount
SDRs moved to discovery
17h → 4m Form-submit to
owner-assigned
Stack Clearbit Apollo n8n GPT-4o-mini Slack HubSpot
Case 03 Internal tooling · Open source Ongoing · Care plan

A boring evaluation harness that catches model drift in CI.

Every model bump — 3.5 to 4o, 4o to 4o-mini — silently broke two to five of our production prompts. We'd discover it from a customer ticket three days later. So we stopped trusting vibes and put the prompts on a CI suite, like any other piece of code.

● Open source 240 evals / 12 systems

The pattern we kept hitting

Prompts lived in code, but their behavior was untested. A model upgrade was a leap of faith — even reverts had no easy way to confirm "back to normal." Eval files lived in someone's Notion. We wanted boring, not clever.

What we did

  • YAML fixtures sit next to the prompt they test — same folder, same PR, same review.
  • GitHub Actions runs the suite on every PR against both pinned and latest model, and reports the diff.
  • A Slack notifier posts a regression summary with concrete failing examples — no "13% drop", just the three prompts that flipped.

Outcome

100% Breaking changes
caught pre-merge
0 Silent regressions
in 4 months
12 → 240 Evals written
once the habit stuck
~90s Full suite runs
per PR
Stack Python pytest PyYAML GitHub Actions Slack OpenAI · Anthropic
01 / 03
Let's build →

Got a workflow that bleeds hours? Let's automate it.

Free 30-minute call. We'll sketch the system, scope the cost, and tell you honestly whether AI is even the right answer.