INNOVATE
Prototype what's next
Rapid R&D on new models, agents and techniques, shaped around African data, languages and channels.
APPLIED AI LAB · NAIROBI
Your AI works.
Now make it fast, cheap and accountable.
We benchmark, optimise and operate production AI workloads for enterprises, on any cloud, any model and any hardware. You get lower latency, a smaller bill and numbers you can defend in a board pack.
inference.trace · p95 latency (ms)
baselineoptimised
BASELINE p95
— ms
OPTIMISED p95
— ms
DELTA
−—%
SIMULATED TRACE · ILLUSTRATIVE ONLY · NOT CLIENT DATA
// WHAT WE ARE, AND AREN'T
Not a GPU cloud.
Not a slide-deck consultancy.
An applied AI lab that measures first, then builds.
// THE LAB
Innovate. Test. Build. Advise.
Bring us an idea, a model shortlist, a system to build or a decision to make. The same lab and the same evidence standard cover all four.
TEST
Prove it before you buy
Independent bake-offs of models, vendors and stacks on your workloads, scored for speed, cost and quality.
BUILD
Ship it for you
Production AI systems, including assistants, agents, automation and integrations, engineered for performance from day one.
ADVISE
Decide with evidence
Vendor-neutral guidance on AI strategy, architecture and spend, backed by what the lab has actually measured.
Our edge across all four is inference and performance engineering. Here's what that means ↓
Three disciplines. One loop.
Benchmark tells us where the time and money go. Optimise moves them. Operate keeps them there as models, traffic and prices change.
01 / BENCHMARK
Measure what's real
Reproducible load tests on your actual prompts and traffic shape, not vendor demos.
- → TTFT, p50 / p95 / p99 latency
- → Throughput & GPU utilisation
- → Cost per 1M tokens, per feature
- → Quality against your own evals
02 / OPTIMISE
Move the numbers
Every lever is proven against quality gates before it ships. Faster never means worse.
- → Model right-sizing & routing
- → Quantisation (FP8 / INT8 / 4-bit)
- → Batching, KV & prefix caching
- → Speculative decoding, prompt compression
03 / OPERATE
Keep them there
InferenceOps: the discipline that stops performance and cost from drifting after go-live.
- → Latency & cost SLOs
- → Spend observability by team
- → Regression gates in CI
- → Capacity planning & runbooks
// TWO TRACKS, ONE METHOD
Wherever your models run, we tune the economics.
// HOW WE ENGAGE
Earn each step. Start small.
Every rung ends with evidence. Either it justifies the next rung or it tells you to stop.
YOU WALK AWAY WITH
// INFERENCE ECONOMICS
What is your token bill hiding?
Plug in your own traffic and two common levers. It's plain arithmetic, so treat it as a sanity check rather than a quote. A diagnostic replaces the guesses with measurements.
Model: spend × (1 − cache hit) × ((1 − routed) + routed × relative price). Quality impact is not modelled here; in an engagement, every lever must pass your evals.
// PROOF BEFORE PROPOSALS
We publish the benchmark before we pitch.
The AfrimationAI Proof Pack contains our method, our harness and our results on open-weight models and public APIs. Your engineers can rerun every number themselves before anyone signs anything.
PROOF PACK · IN PREPARATION · [RELEASE DATE]
AInfrastructure-agnostic
Any cloud, on-prem or API. We hold no hardware we need to sell you.
BReproducible
Versioned harnesses and raw results. Your team can rerun every number.
CQuality-gated
Nothing ships unless it passes your evals. Speed never buys regressions.
DVendor-neutral
We recommend what the data says, including staying where you are.
// WHO WE'RE FOR
Enterprises running AI, or ready to.
Maybe you have workloads live and a bill that keeps growing. Maybe you have an idea that needs testing before anyone commits budget. Either way, we work with you across Africa, from Nairobi.
BANKINGINSURANCETELECOMSPUBLIC SECTORRETAIL & FMCG
PARTNER CHANNEL
Systems integrators & resellers
Bring us in as the inference specialist inside your delivery. You keep the client relationship, and we make the AI layer perform.
Talk partnerships →// STEP 01
Start with
one workload.
A fixed-scope diagnostic on one production path tells you where the latency and spend actually go, and whether it's worth going further.