Verificate HELIX
A hardware-software platform that runs any open model on any CPU — with hallucination prevention built into the inference loop. Sovereign, GPU-class latency, 100% valid JSON, zero egress. Runs on AMD, Intel, IBM Power, and Apple Silicon — Granite 4.0, Gemma 4, and GPT-OSS 120B in production today.
HELIX v1.7 Build #58 · AMD EPYC 9254 (Genoa) · official vLLM GuideLLM benchmark, 640/640 requests, zero errors M
Hallucination prevention, at the inference level.
Most guardrails filter hallucinations after the model has produced them. HELIX prevents them inside the decode loop. Every token is scored for truthfulness in real time; when a trajectory begins to diverge, HELIX steers it back onto a coherence manifold before a false token is ever emitted — on any model, at any temperature, even at 4-bit.
Unified Truth Score
A per-token uncertainty metric fusing semantic entropy with the token's geometric distance from a pre-computed truthfulness manifold — evaluated in the decode pipeline, not after the fact.
Geometric manifold steering
When the Truth Score spikes, latent activations are nudged back toward coherent regions — intervening on just 0.2–2.5% of tokens, preserving the model's own voice.
Model-agnostic & sovereign
Not a wrapper and not a second model. It runs in-engine on any open weights — Granite 4.0, Gemma 4, GPT-OSS 120B — entirely on your CPU, zero egress.
Temperature-invariant reasoning validated to T=3.0 (GSM8K 88.84% stable) · steering confined to 0.2–2.5% of tokens M
Architecture-agnostic. Even better on a Mac.
HELIX runs on whatever silicon you already have — no GPU, no accelerator lock-in. On Apple Silicon it's exceptionally fast, which makes Mac ideal for development and for hybrid deployments that mix architectures.
Your model. Your hardware. Your walls.
HELIX ships as a licensed inference pod you run inside your own infrastructure. There is no Verificate cloud in the loop and no data leaves your boundary — which is what makes it viable for HIPAA, PCI-DSS, and defence-grade air-gapped workloads.
Two live HELIX v1.7 pods are running today on IBM Fusion OpenShift — connect and evaluate them now, then license the pod to run wherever you need it.
- Your Kubernetes cluster
- Your OpenShift environment
- A bare VM — on-prem or sovereign cloud
- Fully air-gapped, zero outbound telemetry
A drop-in OpenAI-compatible endpoint.
Point any existing OpenAI SDK at HELIX and go — no API key required. /v1/chat/completions, /v1/models, SSE streaming, and a grammar-masked JSON mode that guarantees schema-valid output.
- Open OpenAI /v1 chat + models API — no token
- Server-sent-events streaming
- Grammar-masked JSON schema mode
- Per-token cortex steering telemetry
The endpoint is open. Licensed private deployments run entirely inside your own network — nothing routes through Verificate.
License a private HELIX podcurl 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
}'GPU-class latency on a CPU. Measured.
Official vLLM-project GuideLLM harness, identical AMD EPYC 9254 (Genoa) silicon, 32K context, 900 extraction prompts.
| 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) |
Ollama produces ~16-token fragments at 32K; HELIX returns complete, schema-valid extractions — the throughput gap measures fundamentally different outcomes. M
A system-level platform on llama.cpp + GGML.
NUMA pinning + native SIMD
Compute threads pinned to local memory controllers, eliminating cross-die traffic. Architecture-native SIMD GEMM kernels — AVX-512 on x86, NEON on Apple Silicon, VSX on Power — for a 12.9× TTFT advantage over stock Ollama at concurrency 4.
100% valid JSON, always
A logit-bias grammar-masked sampling engine constrains decoding to your schema. 100% valid-JSON compliance across every concurrency tier — automated pipelines never halt on a malformed response.
Quantized accuracy recovered
Sub-layer latent activation steering recovers 98.5% of native FP16 reasoning accuracy at 4-bit — the memory savings of quantization without the logical decay.
True parallel slots
Four KV-warmed inference slots serve concurrent requests in parallel. Sub-950 ms first-token latency at c=1, near-zero variance (TTFT mean ≈ p50).
Model-agnostic — HELIX runs any open weights. In production today: Granite 4.0, Gemma 4, and GPT-OSS 120B. Per-slot KV footprints from ~256 MB at 32K context, architecture-native SIMD compilation, Markov-oracle expert pre-warming, and a 64 GiB OOM safety floor.
Try the live model, then license the pod.
Chat with a live HELIX v1.7 pod running Granite 4.0 on sovereign CPU — then book a call to license HELIX for academic or commercial deployment in your own environment.