Bring Inference Where Demand Is
Training a model is a one-time event.
Running it at scale is where the real engineering starts.
Most AI partners help you ship the POC.
HTEC takes you from silicon to production, and hands it back fully yours.
The Deployment Gap
What stands between a proof of concept and a production system is infrastructure, expertise, and time.
Hidden cost of going live
Moving from proof of concept to production exposes problems no one budgeted for.
Always one model behind
Porting a new model to specialized hardware takes four to twelve weeks, and a newer model ships before you finish.
No common ground
Over 170 inference hardware companies have emerged in two years with no universal software stack to match them.
Two years from silicon to shipping
Done in-house, getting from silicon to production takes twelve to twenty-four months.
What HTEC Delivers
Compiler & toolchain development
Build the software layer between your hardware and your AI workloads, the layer that took today’s market leader a decade to build and that most inference vendors still don’t have.
Workload porting & performance optimization
Architect for model-agnostic stability so absorbing the next model release doesn’t mean starting over.
Hardware-aware cost optimization
Tune for throughput, latency, and cost-per-token from early stages, not after you’re already in production. We instrument the inference stack so token and compute spend are visible by agent and by user as it accrues, day by day.
Multi-model pipeline engineering
Most production workloads run vision, speech, and language models together as a single coordinated pipeline. We tune the full pipeline so the handoffs between models don’t become the part that breaks in production.
Edge & sovereign deployment
Build data residency, access controls, and compliance into the architecture as a first design decision, not a retrofit.
Moving from proof of concept to production exposes problems no one budgeted for.
We work across the entire range, whether you’re building the silicon, integrating it into a platform, or operating in an industry where inference has to stay local and compliant.
Inference silicon and
accelerator vendors
Chip companies that need their hardware in customer hands without building a software organization
Platforms and
OEMs
Teams deploying AI on custom or heterogeneous hardware beyond what standard platforms support
Regulated and
latency-sensitive enterprises
Organizations across regulated industries with strict data residency and audit requirements
Partners
Our engineering work with hardware partners often starts before a tool reaches general release, giving clients capabilities the broader market doesn’t have yet.
If your AI roadmap was designed around a POC, it’s time to pressure-test it. Let’s talk about what production inference actually costs per outcome, not just per token, and what it takes to scale before demand outpaces your budget.
If your AI roadmap was built around a POC, it’s time to pressure-test it.