Latency profiling
Measurement across retrieval, model call, tool execution and network to find the actual cost.
Cost, latency, scale
AI Infrastructure & Model Optimization
Infrastructure and optimisation work that brings AI latency and cost within usable bounds.
What this is
An AI feature that is correct but slow or expensive does not get used. We work on the serving path: caching, context management, model routing, batching, concurrency and deployment topology, including self-hosted models where data residency or volume economics justify it.
The problem
What this service addresses
If more than one of these is familiar, there is usually a case worth scoping.
- Responses take long enough that users stop waiting.
- Model spend grew faster than usage and nobody can explain the curve.
- Load spikes cause rate-limit errors and failed requests.
- Data residency rules rule out the provider APIs currently in use.
Scope
What we deliver
Every item below is a deliverable you receive, not an activity we perform.
- Latency profile across the request path, with the real bottlenecks identified
- Caching strategy for prompts, retrieval results and whole responses
- Context optimisation to cut token volume without losing quality
- Model routing policy per task, validated against your evaluation set
- Concurrency, queueing and rate-limit handling under realistic load
- Self-hosted or private deployment where residency or volume justifies it
- Cost and latency dashboards with per-feature attribution
Capabilities
What the system does
Caching
Prompt caching, retrieval caching and response caching with correct invalidation.
Context optimisation
Shorter, better-targeted context: usually a quality improvement as well as a saving.
Model routing
Task-appropriate model selection with fallback chains, validated against evaluations.
Throughput engineering
Batching, concurrency control, queueing and backpressure under load.
Private and self-hosted serving
Open-weight models on your infrastructure where residency or economics require it.
Scaling and reliability
Autoscaling, provider redundancy and graceful degradation.
Applied
Where it is used
Each of these is a bounded job with a measurable result, which is how we prefer to scope a first build.
01
Interactive latency targets
Bringing a chat or voice experience within the response budget it needs.
02
Cost reduction at volume
Cutting spend through routing, caching and context work without quality loss.
03
High-throughput batch processing
Processing large document or record volumes within a time window.
04
Data residency compliance
Moving inference into a specific region or onto your own infrastructure.
05
Peak load resilience
Handling spikes without rate-limit failures or timeouts.
Architecture
How it is put together
An indicative architecture. The final design follows your systems, data boundary and constraints.
-
01
Request path
Edge, application, retrieval, model call and tool execution, each measured.
-
02
Caching layers
Prompt, embedding, retrieval and response caches with invalidation rules.
-
03
Routing layer
Per-task model selection, fallback chains and quota enforcement.
-
04
Serving
Provider APIs, regional endpoints or self-hosted GPU inference as appropriate.
-
05
Scaling
Queues, autoscaling workers, concurrency limits and backpressure.
-
06
Observability
Latency percentiles, cost attribution, error rates and saturation metrics.
Security & governance
Controls we build in
Access, privacy and oversight are part of the build rather than a later hardening phase.
- Data residency honoured through regional endpoints or self-hosted deployment.
- Provider terms reviewed for training use and retention before selection.
- Spend caps and per-tenant quotas enforced in the serving layer.
- Cache contents scoped by tenant and permission, so caching cannot leak data across users.
- Graceful degradation defined, so a provider outage reduces capability rather than breaking the product.
Delivery
Our approach
How we run the engagement
Discovery
Process walkthroughs, data review, constraints and a written scope you sign off before build.
Architecture
Data model, integration boundaries, hosting and the security model, agreed up front.
Proof of concept
A narrow build on your real data against an agreed quality bar, so the go-ahead is evidence-based.
Development
Two-week increments, each ending in a working deployment and a review.
Testing
Automated coverage on critical paths, evaluation sets for AI behaviour, acceptance testing with your users.
Deployment
Staged rollout into your infrastructure with monitoring and a tested rollback path.
Monitoring & support
Quality, cost and latency tracking after launch, with handover documentation or an ongoing retainer.
Proven work
Products we have built and sold
We are product builders as well as a services team. These are our own commercial platforms, not client references.
Digital sales & support agent
SIZH Virtual Agent
A virtual digital agent for sales, support and reception — trained on your business, live on WhatsApp, web, voice and email.
View product Mobility platformRideOn
A ride-hailing and fleet management platform, now with agentic dispatch, driver support and demand intelligence.
View product Multivendor marketplaceUniBooker
A configurable multivendor service-booking marketplace with agentic onboarding, matching and support.
View product
Questions
AI Infrastructure & Model Optimization — FAQs
The questions we are asked most often about this service.
Should we self-host models?
Only with a clear reason: data residency, very high steady volume, or specialised fine-tuned models. Otherwise provider APIs are cheaper once you account for GPU capacity and the engineering time to operate it. We model both before advising.
How much cost reduction is realistic?
It varies with how much easy inefficiency exists. Systems sending oversized context on every call and using the strongest model for trivial tasks have substantial room. We measure first and show the projected saving against quality impact.
What latency is achievable?
For text, sub-second first token is usually attainable with caching and a well-chosen model. Voice needs tighter budgets end to end. Retrieval and tool calls are often the real bottleneck, not the model.
Can you work within our cloud account?
Yes, and usually we prefer it. We deploy into your AWS, GCP or Azure account using your existing infrastructure-as-code and access controls.
Related
All AI First services
Services that pair with this
MLOps & AI Deployment
Versioning, evaluation pipelines, deployment, rollback and monitoring for AI systems in production.
Read moreManaged AI Services
Ongoing operation, tuning and cost control for AI systems in production.
Read moreArtificial Intelligence Development
Custom AI applications built around your data, your systems and a measurable quality bar.
Read more