Frontier AI systems for high stakes decisions.
Production AI for regulated industries—from models to product.
73% faster claims triage at a Tier-1 European insurer. See the work →
Trusted in regulated industries
We build AI systems where it's hardest to.
Regulated decisions, on-prem deployments, evaluation against real outcomes. We take it end-to-end — research, models, product surface, and operations — because nobody else in this space will.
Adjuster co-pilot, inside the claims tool
A decision surface that reads the evidence packet, surfaces what matters, and shows its work alongside the adjuster. Deployed on-prem, with a continuous eval harness on real claim outcomes.
Settle within policy. Two prior similar claims settled at this band.
The full AI engineering stack, in one studio.
Synthetic datasets. Fine-tuned LLMs. Retrieval-augmented generation. Evaluation pipelines. Red-teaming. Explainability. Each layer designed against the eval that matters in production.
Synthetic datasets, built for ground truth.
Most regulated workflows have no clean dataset. We generate, filter, and adjudicate one — with your operators in the loop and full provenance on every example.
Fine-tuned LLMs, trained on your work.
SFT and DPO over open-weight bases, distilled into production-sized students that match the eval and clear your latency budget.
RAG systems, citation-grounded.
Hybrid retrieval, learned rerankers, and generation that cites every claim. Built on your corpus, evaluated on your queries, deployed where your users actually read.
Evaluation pipelines, not vibes.
Held-out test sets, calibration and abstention checks, behavioral red-teaming, and shadow-mode comparisons against the existing process. Versioned alongside the model and gated in CI — improvements pass, regressions fail.
Adversarial red-teaming, before users find it.
Behavioral attacks, jailbreak suites, and abuse-pattern probes run continuously against every release. We hand you the failure modes — and the patches.
Explainability, by design.
Every decision logs its inputs, prompt, retrieval set, and model version. Every claim cites its source. The trail is replayable for the regulator, the board, or the user whose case it touched.
Deployed inside your perimeter.
Air-gapped, on-prem, VPC, or edge inference. SSO, RBAC, audit logs, and PII boundaries on by default. Weights and data stay where they belong.
Writings on how we ship AI systems.
Articles on AI engineering, product judgment, and executive risk — the three views you have to hold at once when shipping into regulated work. From a team that holds all three.
See all our writingsWhy we built Neuralcraft
We started Neuralcraft to bring production-grade AI systems to teams operating in regulated, high-stakes environments.
Read →Evaluating LLMs in regulated environments
Eval is the hardest part of shipping AI in regulated spaces. Here is how we approach it for healthcare and finance customers.
Read →Audit trails for AI systems
What an auditable AI system actually looks like in production — and the four log streams every team should be writing from day one.
Read →How we design AI products.
Five principles we hold across every engagement. The work is hard; the order isn’t negotiable.
- 01
Strategy
Start with the decision, not the model.
Most AI projects begin with capability. We begin with a decision worth changing — and an honest list of the ones that aren’t.
- 02
Design
The product surface defines the model.
Latency, accuracy, and when to abstain aren’t model properties. They’re product decisions — and they have to be made before anyone trains anything.
- 03
Data
No clean dataset? Then we build it.
Regulated workflows rarely come pre-labeled. Ground truth is something you create alongside operators, with provenance on every example.
- 04
Build
Pick the model that meets the eval.
Frontier, open-weight, or fine-tuned is a budget decision, not an aesthetic one. The eval from step two is the bar. Nothing else qualifies.
- 05
Operate
Day-one eval is month-twelve eval.
The only proof an AI system still works is the same eval that proved it worked. We monitor, retrain, and respond to incidents on a defined playbook.
Ethical and Responsible AI
In regulated work, "responsible" can't be an afterthought layered onto a finished model. It has to be a property of how the system is built — from the data it's trained on to the decisions it surfaces and the trail it leaves behind.
Responsibly sourced data, with provenance on every example.
Licensed, consented, or generated under documented protocols. Every training and evaluation record carries its origin, its labelers, and the policy it was collected under — so the lineage holds up under audit, not just in the slide deck.
PII scrubbed, features chosen on purpose.
Personal identifiers are removed at ingest, not at inference. Models see only the features the decision actually requires — protected attributes excluded by design, proxies tested for, and access boundaries enforced inside your perimeter.
Auditable end-to-end, every decision and explanation.
Every output logs its inputs, prompt, retrieval set, model version, and the explanation it gave. The trail is replayable for the regulator, the board, or the customer whose case it touched — months or years after the fact.
Careers at Neuralcraft
We're hiring engineers and researchers who want to ship into regulated work — where the eval is the bar, the audit trail is the deliverable, and the model is the easier half.