jai works
engineering · analytics · data science · one engine

Stop deciding what to try. Try everything.

Your AI coworker for engineering. analytics. data science. engineering. It simply works.

Trying used to be expensive — so the meeting was about which bet to make. Jai runs on your infrastructure and turns tickets into shipped work, in parallel, at machine pace. Enumerate the plausible options. Run them all. Let the results decide.

Join the waitlist→ See the math
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00 not just for one team

One engine, three disciplines.

The mechanics are the same. The work isn't. Jai meets engineers, analysts, and data scientists in the same workflow they already use — and turns the backlog into shipped work.

engineering

From issue to merged PR.

  • migrations, refactors, dependency bumps
  • bug fixes with regression tests
  • API endpoints + clients + docs in one PR
  • flaky test triage and quarantine
#4128 migrate auth middleware to JWKS, write tests ✓
analytics

From question to dashboard.

  • SQL pulls, cohort definitions, funnel cuts
  • ad‑hoc memos with charts and assumptions stated
  • dbt models + tests + lineage updates
  • recurring weekly reads, on autopilot
#2207 retention by acquisition channel, last 12 weeks ✓
data science

From brief to shipped model.

  • sweeps, ablations, baselines at parity FLOPs
  • full pipelines: data → features → train → eval
  • fairness audits, drift checks, retrain triggers
  • 150 experiments in a week is ~300 days of human work
#1287 sweep · lr × wd × dropout · 64 configs ✓

Same engine. Same audit trail, same budget controls, same GitHub‑native workflow. The only thing that changes between lanes is the label on the ticket.

01 the math

One week. 150 tickets closed. Mixed across engineering, analytics, and DS. ~2 days per project at human pace — do the multiplication.

0
tickets closed
one calendar week, mixed disciplines, one team
0days
of human work compressed
at the industry baseline of ~2 days / project
0%
ticket‑referenced
every run, PR, and artifact links back to its issue

Different problems. Different stacks. Different teams. Same machinery, same trail of breadcrumbs, same week.

eng#4128

Migrate auth middleware → JWKS

Rotation handled, 31 tests added, zero‑downtime cutover plan in the PR.

merged
data#2207

Retention by acquisition channel

Last 12 weeks. Paid social down 6pt; organic + referral compensating.

merged
ds#1287

LR × WD × Dropout sweep — 64 configs

ResNet‑50 on ImageNet‑1k subset. Best @ lr=3e‑4, wd=0.05.

merged
eng#4135

Flaky test triage · payments suite

14 quarantined, 9 root‑caused, 5 fixed. PR per fix with repro.

merged
data#2214

dbt model · orders_enriched

Lineage updated, freshness checks, 6 tests. Looker tile auto‑refreshed.

merged
ds#1304

Attention‑head ablation

Drop heads {3,7,11} → −0.2 pt. Drop {1,5,9} → −1.8 pt.

running
data#2221

Funnel: signup → activated, by plan

Free → Pro activation up 11% after onboarding change. Memo + chart shipped.

merged
ds#1311

CIFAR‑100 augmentation grid

RandAugment(N=2, M=9) wins by 0.7 pt, no extra train cost.

merged
eng#4140

Rate‑limit middleware · per‑org buckets

Token bucket, redis backend, soak‑tested at 12k rps. PR + runbook.

merged
ds#1322

Fairness audit · credit model v4

Demographic parity Δ=0.03 → 0.01 with reweighing. PR open.

running
data#2229

Weekly read · revenue + churn

Generated, charts attached, deltas vs last week called out. Recurs every Mon.

merged
eng#4146

Bump pg driver, fix N+1 in /orders

p99 down 240ms → 38ms. Migration + perf test included.

merged

One week. Twelve sample tickets across engineering, analytics, and data science. Each closed with a comment, a PR, an artifact, and a green check.

02 the mindset shift

A complete mindset shift. When trying is cheap, you stop optimizing the guess and start running the options.

the old world

Trying is expensive.

Compute is rationed. People are scarce. Every option you run is one you didn't. So the meeting is about which one to bet on. We argue. We pick. Mostly we pick wrong, and we learn it months later.

  • long planning, narrow execution
  • three ideas tested, twenty shelved
  • opinion beats evidence
with jai

Trying is cheap.

Enumerate the plausible options. Run them all. Read the results. The meeting becomes a review, not a guess. You stop optimizing the decision and start optimizing the work.

  • short planning, wide execution
  • twenty ideas tested, the best three shipped
  • evidence beats opinion
"We used to spend a week choosing the experiment. Now we spend an hour listing them, and a day reading what came back."
03 how we onboard you

We define the flow. We set it up. It simply works.

We don't drop a tool on you and say good luck. Onboarding is four steps, runs in days, and ends with Jai living quietly inside your stack — picking up tickets and shipping work.

  1. 01

    We define the flow with you.

    One short call. We map your real workflow — repos, ticket conventions, approval gates, who reviews what. We turn that into a profile Jai will follow. No abstract change management; just the way your team already works, written down.

    output workspace profile
  2. 02

    We set up the environment.

    Mongo, orchestrator, worker pool, egress proxy — installed inside your VPC. Secrets stay in your secret store. We wire GitHub webhooks, bring up monitoring, and run a dry‑run ticket end to end before we hand you the keys.

    installs in your VPC, on day one
  3. 03

    It runs on your infra. Always.

    Your code never leaves your perimeter. Your data never sees ours. The model API is the only egress, and it goes through a proxy you own. We can't read a prompt even if we wanted to. Compromise blast radius is one short‑lived worker token.

    byoc your cluster · your secrets · your spend
  4. 04

    You file tickets. Jai ships.

    From here it's a tool, not a project. File an engineering ticket, an analytics ask, an experiment brief — same flow. Jai plans, runs, PRs, and closes. Your team reviews and merges. It simply works.

    steady state tickets → PRs, on autopilot
04 why teams trust it

Trackable. Controllable. Boring on purpose.

Speed without governance is a footgun. Jai's whole point is that you can move 150× and still answer "why did we ship this?" in under a minute.

Trackable

Every experiment is a ticket. Every run is an artifact bundle — config, seed, dataset hash, code commit, metrics. Nothing gets lost in a notebook on someone's laptop.

  • config + seed + commit on every run
  • auto‑linked PR ↔ ticket ↔ artifact
  • diffs across runs, not screenshots

Controllable

You set the compute budget, the data scopes, the acceptance criteria, the merge policy. Jai stops when it should, asks when it must, never surprises the ledger at month‑end.

  • budgets per ticket and per workspace
  • policy‑gated tool access
  • human approval where you want it

Auditable

Append‑only history of every decision, every prompt, every tool call. An auditor or a curious VP can replay the week without you opening Slack. Boring is a feature.

  • append‑only run + conversation log
  • signed PRs, traceable to a ticket
  • nothing happens off‑record
05 the difference, plainly

What a week looks like, with and without.

without jai

~8 tickets shipped

  • Mon · planning meeting about the planning meeting.
  • Tue · engineer waits on review. Analyst waits on data. DS waits on quota.
  • Wed · half the team is on the wrong Slack thread.
  • Thu · the dashboard breaks. Nobody knows which commit.
  • Fri · status doc reads "in progress" for the third week.

≈ 8 tickets · 3 grumpy ICs · 0 audit trail

with jai

150 tickets shipped

  • Mon · 40 tickets filed across eng / data / DS. Budgets set. Walk away.
  • Tue · 60 runs done. Six PRs already merged. Analyst memo posted.
  • Wed · pivot. File 25 more tickets based on what's winning.
  • Thu · weekly review reads itself — every claim is a clickable artifact.
  • Fri · ship‑letter writes itself from the closed tickets.

≈ 150 tickets · 100% reproducible · 1 calm team lead

private design partnership · cohort closing soon

Stop bottlenecking on humans‑per‑ticket.

We're onboarding a handful of teams — engineering, analytics, data science, or all three. We define the flow, set up the environment, and it runs on your infra. You file tickets. Jai ships. Drop an email.

No drip. No newsletter. A human reads every reply.