A fleet of Tailors takes your business objectives and runs them continuously — adapting, improving, and acting. No babysitting. No finish line.
Most AI tools wait for instructions. You still need someone to give them.
Give sshop an objective once — a margin floor, a competitive position, a product signal to watch — and the Tailors run it. Every hour. Every day. Getting better each time.
Not a task. A function.
What you deploy
Today
Engineers handle bug fixes, architecture decisions, dependency upgrades, and large-scale refactors manually. A major refactor is a sprint. A breaking dependency is a day of careful patching.
With sshop
After ingesting your codebase, Tailors understand your architecture, conventions, and test suite. Dependency upgrades, refactors, and bug hunts run as thousands of parallel branches — each scored by running your tests. The winning approach arrives as a reviewed, tested PR.
Today
A pricing analyst monitors competitors, demand signals, and conversion rates. They propose changes, test them, and measure impact.
With sshop
Tailors monitor the same signals continuously. They propose changes, run A/B tests against real traffic, measure revenue impact, and adjust. Your team sets the margin floor. The Tailors handle the rest — 24/7.
Today
A product manager synthesizes support tickets, churn surveys, and competitor updates into a prioritized roadmap.
With sshop
Tailors ingest all of it, generate feature hypotheses, build testable mockups, and surface the highest-impact candidates weekly. Your team reviews the outputs, not the inputs.
Today
A procurement manager tracks stock levels, drafts purchase orders, manages supplier relationships, and reconciles inbound deliveries.
With sshop
Tailors monitor stock velocity and lead times. They send purchase orders at the right moment, flag supplier issues, and reconcile deliveries. Your team handles exceptions.
Before execution
An ingest phase happens before task execution. Agents crawl your accessible systems and build a rich knowledge base that every Tailor queries at runtime.
Agents crawl your accessible systems — Git repos, docs, API specs, database schemas. You control what they can reach.
You select what to ingest and set access controls: read-only, rate limits, scope boundaries. Nothing runs without your sign-off.
Everything extracted into a knowledge graph, searchable corpus, and embeddings. The structure of your systems becomes queryable.
When Tailors run, they query this knowledge. They know your conventions, your architecture, your current implementation before writing a single line.
Why this matters
Process
Task description → eval criteria generation → parallel execution → scoring → next generation. Zero human configuration at any step.
One sentence. Natural language. 'Manage our inventory reordering.' That's it.
No eval script to write. The Eval agent derives its own success metrics from your description — informed by your ingested knowledge graph. It knows what 'good' looks like for your company.
Not 10, not 50 — thousands of independent Tailors attempt solutions in parallel on Pinstripes. Each queries your domain knowledge corpus. No cross-branch communication. No echo chambers.
Every branch is scored against the criteria the Eval agent wrote. No human review required. Selection is deterministic.
The best branch becomes the baseline. The next generation starts from there. Score is guaranteed ≥ previous — monotone by construction.
score(gen n+1) ≥ score(gen n) — guaranteed by construction
The guarantee
Each generation seeds from the previous winner. The next generation cannot start below the current best score. Your system only moves forward.
This isn't a probabilistic claim. It's a property of the architecture. Beam search over a scored population with elitist selection gives you monotone improvement by construction.
∀n: score(gen n+1) ≥ score(gen n)
∀n: best(gen n+1) ≥ best(gen n)
monotone by elitist beam selection
Benchmark — Task 077
Three generations. Thousands of branches per generation. Each generation guaranteed ≥ previous.
Visualized
Every generation, thousands of independent Tailors attempt solutions in parallel. Eval scores each. The best beam seeds the next round — guaranteed at or above the current best.
Usage
$ sshop ingest --source git,confluence,postgres
→ Discovery: crawling accessible systems...
847 source files (git)
234 doc pages (confluence, read-only)
23 API schemas (openapi)
12 DB schemas (postgres, read-only)
→ Knowledge build...
12,400 embeddings · 3,847-node graph
✓ Domain context ready
$ sshop deploy "optimize our checkout conversion rate"
→ Eval agent generating success criteria...
[domain context loaded — pricing system architecture understood]
14 metrics derived · baseline: 0.448
→ Gen 1: 2,048 Tailors on Pinstripes...
✓ Best: 0.621 (+38.6%) guarantee ≥ 0.448 ✓
→ Gen 2: seeding from 0.621...
✓ Best: 0.651 (+45.3%) guarantee ≥ 0.621 ✓
→ Gen 3: running [0.667 best · 2,048 active]_
Transparency
Every decision the Tailors make is logged, auditable, and backtrackable. If something looks wrong, you see exactly what it did and why. You're not trusting a black box — you're reading a paper trail.
You set the margin floor, the risk tolerance, the criteria that matter. The Tailors optimise within those bounds, and every step is visible.
── checkout-conversion / gen 3 ─────
14:02:01 EVAL criteria: 14 metrics active
14:02:03 GEN 3 2,048 Tailors spawned
14:02:31 Tailor #1847 scored 0.667 ← leading
14:02:31 Tailors #0-2047 except #1847 dropped
14:02:31 score(gen 3) ≥ score(gen 2) ✓
14:02:34 GEN 4 seeding from Tailor #1847
──────────────────────────────────
runtime: 31s/gen cost: $0.041/gen
Thousands of Tailors actually fire simultaneously. Beam search is real parallelism — not 2,048 requests queued one at a time. Pinstripes has no rate limits, no concurrency caps. You pay per token.
The unlimited concurrency isn't a nice-to-have. It's load-bearing. Queue any branch and you've broken the model. We built orchestration and inference at the same company so they work together.
sshop is free. Open source. Runs on Pinstripes starting at $5/month.