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Threadbaire

Pilot

Make outputs comparable across models in 2–3 weeks.

Threadbaire is an LLM-agnostic memory layer. The Pilot runs LLM-agnostic replay end-to-end and produces AI answer receipts. You get the full spec: canonical names (MPR), adapter maps, and verifier templates.

Choose a track See the Demo Read the Approach

Data stays in your stack. We never train on your content. Privacy · DPA available on request.

No URL-style secrets. Pilots run in your tenant with scoped keys or sandbox exports. Already using an OpenAI-compatible proxy/context extender? Threadbaire rides on top and adds decision receipts.

Incident Audit

A fast check to show where context is dropping and what to fix.

You’ll get: 1-page variance summary · Drift notes · Minimal MPR for your slice · Sample AI answer receipts · Manifest (models/versions + capture hashes)

  • How we do it: Replay 1–2 real threads across two models with light adapters.
  • When: ~5 business days after we have inputs.
  • Scope: Standard scope = up to 2 real threads, 2 models, OpenAI-compatible proxy, basic receipts.

Most teams land between €2–4k (current pilot cohort; reviewed quarterly — USD on request).

Request Paid Pilot

Strategic Memory Sprint

A hands-on pilot to stand up a working slice with names, adapters, and guardrails.

You’ll get: Working slice · Canonical names (MPR) · Adapter maps · LLM-agnostic replay across GPT & Claude · AI answer receipts · Manifest (models/versions + capture hashes) · Audit kit · Week-one plan

  • How we do it: Set canonical names (MPR), map inputs/outputs, add receipts and simple governance. Compare across two models.
  • When: 2–3 weeks end-to-end.
  • Scope: Standard scope = 2 threads, 2 models, OpenAI-compatible proxy, basic governance.

Most teams land between €7–12k (current pilot cohort; reviewed quarterly — USD on request).

Packaged implementation; IP & methods remain Threadbaire’s.

Request Paid Pilot

Proof highlights

  • LLM-agnostic replay
  • AI answer receipts (peek)
  • ≤10% variance target
  • Runs in your tenant
  • DPA on request
  • Manifest + capture hashes (Pilot pack)

Common result: fewer re-explanations across tools and faster, defensible decisions.

How it works

  1. Kickoff (30–45 min). We confirm your goal and pick 1–2 real examples you already run e.g., a support ticket, spec/doc, or chat thread. Together we define what “good” looks like.
  2. Run the check. We plug a small, safe slice (a limited part) of that workflow into your tools and run the same inputs through two AI models (for example, GPT and Claude). We note where they match, where they drift, and why.
  3. Report & next step. You get side-by-side results, a 1-page variance summary, and a simple plan: keep as-is, extend to more cases, or stop.

Each run yields AI answer receipts (who/what/why/source) so results are auditable and comparable across models.

Target: ≤10% variance on timeline events; next-action aligned with objective (plainly: both models agree on what happened and what to do next).

We share the full templates and spec after a short NDA.

If you already use an OpenAI-compatible proxy/context extender, we run on top of it, no change to your infra.

Best for PM/ops teams losing context across tools/models.

Requirements: what we’ll need from you

  • A real slice. 1–2 threads you already use (docs, tickets, chat, PRs).
  • One point person. Someone who can unblock small questions.
  • Access path. Read access + permission to run evals with your model keys (or a test tenant).
  • Light NDA. Covers the audit kit and adapter details.
  • Identity & secrets. No URL-style secrets. Scoped API keys or sandbox exports only.
  • Risk & data handling. We work in your keys/tenant (read-only where possible). No data retained after the Pilot.
  • Compliance & retention. We support export/delete windows. Work runs in your keys/tenant; no data retained after the Pilot.
  • Time box. Agree dates so we can land a clear decision.

Which one should I pick?

Choose Incident Audit if…

  • You’re not sure the problem is real yet.
  • You need a quick read on drift and variance.
  • You want fixes you can try now.

Choose Sprint if…

  • You already feel the pain and want a working slice.
  • You need like-for-like outputs across models/vendors.
  • You want the audit kit and names baked in from day one.

FAQ

Do we sign an NDA on the page or later?

After you request access. Short, light NDA only for Pilot materials.

How long is the Pilot?

Most teams finish in 2–3 weeks end-to-end.

Which models/tools are compatible?

GPT, Claude, and local models. Works with OpenAI-compatible proxies/context extenders. Adapters align names so outputs are comparable.

Does it work with our OpenAI-compatible proxy/context extender?

Yes. We run on top and add AI answer receipts and strategic recall. Pilots run in your tenant with scoped keys; zero retention after the Pilot.

How do you handle compliance & retention?

Receipts include lineage (who/what/why/source). We support export/delete windows so audits aren’t a scramble. Pilots run in your tenant; no data is retained after completion. See Privacy. DPA available on request.

What is LLM-agnostic replay?

Running the same portable memory slice across two models (e.g., GPT & Claude) with the same prompt to verify comparable outputs. Open the receipts to see lineage.

What’s in the manifest?

The Pilot pack includes a machine-readable manifest that records models, versions, prompts/templates, pass-criteria versions, and capture hashes for the frozen outputs shown on the Demo. It’s the receipt for “what was compared, exactly,” so audits and re-runs are clean.

Do you accept URL-style secrets?

No. Pilots run in your tenant with scoped API keys or sandbox exports. We never store your data and keep zero retention after the Pilot. See Privacy.

Request Paid Pilot

Tell us where you’ll evaluate. We’ll follow up with next steps and a short intake.

Limited slots each month. Questions first? Email for a 15-min fit check.

Budget
See the Demo Read the Approach