LLM & speech inference engineering

Lower inference costs. Faster responses.

We help AI product teams cut API and GPU costs, reduce response times, and run suitable models on their own infrastructure.

Start with one workload. Pay only if you want to keep iterating.

Where we can help.

Your product already uses AI. We work on what it costs to run, how quickly it responds, and how it holds up as traffic grows.

Spend less per result

Compare your current API bill with alternative model routes and optimised LLM or speech endpoints. Check quality alongside the total operating cost of each option.

LLM cost optimisation

Reduce response delays

Find where time goes across ASR, LLM and TTS. Improve first-token and first-audio latency, and check response times when traffic peaks.

Speech latency optimisation

Serve more on each GPU

Serve more requests on your existing hardware. Tune batching, quantisation, memory use and scheduling while checking throughput and latency.

GPU inference optimisation

Deploy on your infrastructure

Run suitable models in your cloud or on-premises. Compare quality, capacity and operating costs before deciding what to move.

Private AI deployment

Speech API migration

Move from speech APIs like ElevenLabs to a stack built around models you control. We compare voice quality, language support and operating cost, then integrate suitable speech endpoints and plan a staged migration.

Private coding assistants

Run private coding assistants with open models for selected engineering tasks. We help reduce reliance on paid tools like Claude Code and Codex where an open-model workflow meets your quality, response-time and cost requirements.

One workload. Measured changes.

We scope the work around your traffic, hardware and quality requirements. Scope, price and timeline are agreed before implementation.

Establish a baseline

Measure cost per successful result, latency, throughput and output quality on representative requests.

Choose the experiments

Identify two or three changes worth testing, from model routing and caching to GPU serving configuration.

Compare the results

Use the same inputs and traffic conditions. Check quality and errors alongside cost and speed.

Put improvements to work

Implement the changes that pass the checks. Hand over deployment configuration, monitoring and a rollback plan.

For teams running AI in production.

We work with CTOs, founders and ML or platform teams. Your models may run behind an API, on your GPUs, or across both.

Voice agents

Less waiting between turns

For teams running phone agents and conversational products. Look at the full ASR–LLM–TTS path, cost per successful call and conversation quality.

LLM products

Control the cost of each workflow

For document, legal, finance and coding products. Find repeated calls, oversized context and model choices that add cost or delay.

Speech & clinical software

Process more audio reliably

For transcription, call analysis and clinical scribe teams. Measure cost per audio hour, turnaround time and transcription quality together.

Private AI & model teams

Make serving fit your hardware

For teams shipping their own models or private deployments. Work on GPU capacity, memory limits, worker startup and deployment requirements.

Building AI for clients? We can handle inference engineering alongside your team. Discuss a client project →

Which workload should we look at?

Tell us what your product runs on, where cost or latency is causing problems, and what good output looks like. We'll discuss a focused review and the experiments worth testing.

Book a 30-minute call

Prefer email? [email protected] A short description of the workload is enough.