How confident are you
in the response you are
getting from AI?
Not ChatGPT. Not Claude. Not Llama. HELIX is the software that runs your open model on your machines and attaches a 0–100% confidence score to every answer — so you can deliver when high, confirm or escalate when uncertain, and block when too low.
The percentage is honest: an 80 score is right about 80% of the time — usually within ~2.6 points of reality, vs ~30 when the model rates itself · under 1% CPU overhead, ~0% on GPU M · your app decides; HELIX does not rewrite answers
The answer looks right. That is exactly the risk.
How confident are you in the response you are getting from AI?
You can see fluent, professional text. You cannot see whether the model is grounded or guessing. HELIX makes that risk visible as a score.
What is the impact of that answer being believable but wrong?
A wrong answer that sounds correct spreads further, faster — into customer replies, internal guidance, and automated workflows — before anyone notices.
How impactful would a confidence score be that you can trust?
It turns AI from “hope this is fine” into an operating control: deliver, confirm against your systems, escalate to a stronger model, or stop — using thresholds you set.
And if that score came with faster answers on smaller infrastructure?
You gain control without a cost or hardware tax. HELIX runs your open model and scores every response on GPU, CPU, edge, and IoT.
Escalation only works if the cheap model knows when it is guessing.
Many teams start with a small model and escalate to a frontier model when the job gets hard. The silent failure: the cheap model is confident and wrong — so it never escalates, and the premium tier never sees the risk.
Confident wrong never escalates
Self-ratings, vibes, and “sounds unsure” miss fluent fabrications. Wrong facts and tool steps get written into the agent trail. By the time a premium model is called, the context looks rich — and poisoned. Risk sat on the cheapest tier the whole time.
A trustworthy score on every tier
HELIX attaches a calibrated 0–100% confidence score to every response — cheap, mid, or premium. High → deliver. Mid → confirm or escalate. Low → escalate or block. You escalate on risk, not vibes — so the cascade does what the architecture promised.
Answers people read and act on.
HELIX scores every response from the model you run — chat, knowledge search, assistants, agents, and multi-tier serving paths where cost control depends on knowing when to escalate.
Customer and staff chat
Support and internal assistants where a fluent wrong answer becomes a business incident.
Document and knowledge Q&A
Answers grounded in your files or knowledge base — with a score when grounding is weak.
Model cascades & agents
Cheap-to-premium escalation and tool-using agents — score every step so risk does not hide on the lowest tier.
Private / regulated serving
Run the model you choose inside your cloud or air-gapped boundary — with a trust score every response.
Assistants that call tools
Agents that look up systems or take steps — score risky tool use as that track locks.
Vision and multimodal
Describe images and documents with measured grounding paths — text detection already locked.
A confidence score you can act on — without slowing the service down.
Most software that runs open models is built for speed alone. Every answer still sounds sure — whether it is right or wrong. HELIX adds a trustworthy score while it runs the model.
A score you can trust, at almost no cost
A 0–100% confidence score on every response. The percentage tracks real correctness — not a guess the model invents about itself — roughly 12× closer to reality than self-rating. Under 1% compute on CPU; near-zero added cost on GPU. The model weights are not changed.
Faster answers on the hardware you have
Built for the model and machine you deploy — measured faster than stock open baselines under load. Confidence and performance in one serving layer, including smaller CPU and edge footprints.
Familiar chat API for assistants
OpenAI-compatible chat so your existing apps can point at HELIX. Tool calling for assistants and agents that look up your systems. Risky tool-use scoring ships as measurements lock.
Most buyers only know cloud AI APIs — or open models.
That is normal. The piece in the middle — the software that turns an open model into answers on your computers — is rarely explained outside engineering teams. Here it is simply.
Cloud AI APIs
SaaS APIs. You send a question to their service. Easy to start — your prompts and data leave your company.
Open models
A trained AI you can license or download and keep. That file is the “brain.” It does not answer anyone until something runs it.
HELIX
A containerized image you run wherever you need inference — cloud, on-premises, or edge. Runs your model, scores every answer. Your data never leaves your company to reach us.
Picture it this way. ChatGPT and Claude are restaurants — you visit, they cook, your order leaves the building. An open model is the recipe. HELIX is kitchen equipment we deliver as an image — you install it in your building. Answers stay home. Every plate gets a confidence score.
A confidence score you can trust — and act on.
When an answer sounds right but is not supported by the facts, documents, tools, or reality, that is the failure mode organisations feel first. HELIX does not silently rewrite answers. It returns a calibrated 0–100% confidence score and lets your systems deliver, confirm, escalate, or block.
One score leaders understand
Risk is assessed as the answer is produced, then summarised as a 0–100% confidence score your teams can act on.
A score — not a silent rewrite
HELIX does not quietly edit answers behind the scenes. It scores risk; your application decides what happens next.
You set the rules
Deliver when high. Confirm or escalate when uncertain. Block when too low.
Light enough to leave on
Confidence scoring at under 1% compute cost — near-zero added cost on GPU in measured runs.
We measure residual risk at a review budget — we do not claim AI never errs. Numbers on Benchmarks M
Different failure modes. One score your systems can use.
Simple keyword filters miss confident fabrications — nearly half of real failures still look “sure.” HELIX’s score is built from multiple signals, not fluency alone.
Guessing under uncertainty
The model does not know — and invents a fluent answer anyway. On the page it still looks like normal prose.
Confident fabrication
Sounds sure — invented names, facts, or citations. Asking the model to check itself often still says “fine.”
Internal disagreement
Parts of the model disagree before the final wording. Judging only the finished text misses the split.
Ignoring the source material
Skips the document or tool result and fills gaps from memory — even while citing sources.
Out of date, still fluent
RWas true once; no longer (prices, roles, “latest”). Roadmap coverage until measurements lock.
Wrong about an image or audio
Describes what is not in the file. Text-only checks do not catch it.
Faster inference — including on smaller infrastructure.
HELIX is built to run where the work already lives: full GPU deployments when you need them, and first-class CPU, edge, and IoT paths when smaller infrastructure is the right call — without making “cheap hardware” the whole story.
Your data never leaves
your company.
ChatGPT and Claude sell you an API — prompts go to their cloud. HELIX is the opposite: we license a containerized image you run wherever you need inference — Kubernetes, OpenShift, VM, cloud account, or air-gapped network. There is no Verificate SaaS in the path of your production answers.
The live demo is hosted so you can try HELIX. Production is always your image, in your environment.
- Your Kubernetes or OpenShift cluster
- Your cloud account / customer partition (AWS, Azure, GCP)
- A bare VM — on-prem or private cloud
- Fully air-gapped, zero outbound telemetry
An OpenAI-compatible endpoint — on your machines.
After you deploy the image, point existing SDKs at your HELIX endpoint — not a Verificate cloud. /v1/chat/completions, /v1/models, streaming, schema-valid JSON — plus a confidence score your application can route on.
- OpenAI-compatible /v1 chat + models API
- Server-sent-events streaming
- Grammar-masked JSON schema mode
- Per-response confidence for deliver / confirm / block
curl https://<your-helix-pod>/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{
"model": "ibm-granite/granite-4.0-h-small",
"messages": [{"role":"user","content":"Extract as JSON..."}],
"response_format": {"type": "json_object"},
"stream": true
}'Speed you can measure — trust is the new column.
Serving latency on official vLLM-project GuideLLM harness, identical AMD EPYC 9254 (Genoa), 32K context. Engine benchmarks elsewhere stop at tok/s — HELIX adds the confidence surface.
| System | Concurrency | TTFT p50 | Valid JSON | Tokens / request |
|---|---|---|---|---|
| Verificate HELIX v1.7 | c=1 | 940 ms | 100% | ~85 (complete) |
| Verificate HELIX v1.7 | c=4 | 2,469 ms | 100% | ~87 (complete) |
| Stock Ollama (32K) | c=1 | 8,416 ms | ~84% | ~16 (fragment) |
| Stock Ollama (32K) | c=4 | 31,816 ms | ~84% | ~16 (fragment) |
HELIX v1.7 Build #58 serving numbers · v1.8 adds the confidence runtime on this spine M · see Benchmarks
Built as an inference engine — not a chatbot wrapper.
Tuned to the model you run
Serving paths built for the model you deploy — not a generic runtime that leaves accuracy and speed on the table.
Valid JSON, every time
Responses constrained to your schema so integrations do not break on malformed output — even under load.
Built for concurrent users
Warm request slots keep first-token latency steady when many people hit the same service at once.
OpenAI-compatible API
Point existing SDKs at your HELIX deployment — familiar chat and models endpoints inside your environment.
Production model examples today include Granite 4.0, Gemma 4, and GPT-OSS 120B — the engine is calibrated per model family.
Try the live engine, then license the pod.
Try the public demo, then license the HELIX image for academic or commercial use — deployed in your cloud or on-premises. Production answers stay in your company.