Home / Products

Seven products. One engine.

WRIT is our engine — a runtime that lets a deployed language model acquire bounded, reversible behavior with no training run. Each product below is what WRIT does for a specific buyer, benchmarked end-to-end. Status is honest: Live runs today, Pilot is build-with-us, Validated is benchmarked in the lab.

3Live · runnable today
2In pilot · build with us
2Validated · lab benchmarked
WRITEngine · technology page
LIVE · 3

Live — runnable today.

Working today. Demo casts, browser demos, or live integrations — we can show you the product running on a call.

KnowledgeLive

DeltaWrite

A live knowledge layer for frozen models.

Add new facts to a deployed language model on demand. The facts persist across sessions, can be revoked at any time, and leave the base model bit-identical on everything else.

Who buys itTeams running open-weight LLMs in production who need the model to reflect new facts without a training run.
Paraphrased recall · 553 enterprise facts · Qwen-7B
0.940
MMLU drift on the unmodified baseline (15,498 prompts)
0.000
vs LoRA at matched accuracy: capability cost
−0.20 vs 0.00
View product →
KnowledgeLive

LiveContext

Your assistant always knows the latest number.

Wire a live data source — a price feed, an API, a database — into a deployed assistant so the model's view of that fact refreshes between turns. Unrelated queries stay bit-identical to the base model.

Who buys itAnyone building an LLM-backed assistant on top of fast-moving data: financial dashboards, ops tooling, monitoring, customer support that needs live order status.
BTC chat refresh interval
5 s
Chat assertion suite (Qwen-7B + live Coinbase feed)
5 / 5 pass
Behavioral change on unrelated turns
bit-identical
SafetyLive

Shield

Patch a jailbreak in seconds. Revert it just as fast.

Harden a deployed model against a specific jailbreak family in 33 seconds. Apply and revert are both instant — no retraining, no model release. Benign queries stay untouched.

Who buys itFrontier-model providers and downstream deployers who need to respond to a newly disclosed jailbreak in hours, not weeks. Red teams running before-and-after evals.
Static jailbreak families (5 incl. 2 unseen) · attack success
15.8% → 0.6%
GCG held-out attacks (75 prompts) · attack success
65.3% → 0.0%
Setup wall-clock; spurious refusal on benign inputs
33 s · 0%
PILOT · 2

In pilot — build with us.

Benchmarked end-to-end with strong numbers. Missing only the wrapper or UI to be a product — we co-build the deployment with a design partner.

SafetyPilot

Erasure

GDPR-grade right-to-erasure for deployed models.

Remove a specific record from a deployed model's knowledge surface in microseconds. Verified gone, other knowledge intact, general capability unchanged. Reversible if the deletion was made in error.

Who buys itRegulated enterprises with data-subject-deletion obligations under GDPR Article 17, CCPA, and emerging AI-specific data laws. Compliance teams. Any deployment that ingested user data into a fine-tune.
Per-record erasure (Qwen-7B, CounterFact, N=100)
36.9 µs
ForgetScore · RetainScore · ΔMMLU
1.000 · 1.000 · 0.000
Wall-time advantage vs ROME / MEMIT
660,000× – 14,000,000×
SafetyPilot

Guardrails

Multiple behavioral guardrails on one model. Each a hard off.

Run several independent capability fences on a single deployed model — unauthorized financial advice, medical diagnosis, dangerous-skill instructions, brand-prohibited topics — without any one of them leaking into normal use.

Who buys itRegulated-domain deployments where a model must categorically refuse a list of capabilities. Enterprise platform teams gating a shared LLM.
Per-fence attack success (5 stacked fences, 200 attempts each)
0 / 200
Same prompts on the unmodified baseline
66.5%
Adjacent benign queries answered correctly per fence
30 / 30
VALIDATED · 2

Validated — lab benchmarked, productization in progress.

Lab-benchmarked with reproducible results. The construction is proven; the productization path is open.

BehaviorValidated

Persona

A custom voice on the topics you choose. Untouched everywhere else.

Give a frozen model a custom voice, persona, or behavior on a keyword or topic — without changing how it answers anything else. Trigger by keyword, topic, or pattern; the rest of the model is unchanged.

Who buys itProduct teams shipping branded chat experiences. Studios building character agents. Internal tooling teams that need persona consistency across model upgrades.
Eleven-test behavioral suite (Qwen-7B)
10 / 11 pass
Control leaks on out-of-distribution prompts
0
Cross-scale parity (Qwen 1.5B → 7B)
no regression
KnowledgeValidated

Anchor

When the prompt fights you, the prompt loses.

Plant a fact into the model so it dominates contradicting context, paraphrased queries, and adversarial rephrasing — including long inputs and RAG injections where in-context prompting collapses.

Who buys itRAG pipelines where retrieved text contradicts brand mandates. Agent stacks where tool output overrules system prompts. Regulated deployments where a house fact must beat any user phrasing.
Hit rate vs in-context prompting (matched conditions)
84% vs 5%
Advantage growth (1 → 1,000 simultaneous facts)
1.86× → 18.5×
Time-to-first-token at 1,000 facts vs in-context
16 ms vs 343 ms
Across every product above

No retraining. No measurable quality cost. Reversible by design.

Contact

Pick the one that maps to your problem.

We respond within one business day. NDA not required for the first call. Pilots, design partnerships, and integration conversations are all open.

See the engine →