Make AI responses faster and more economical without losing the quality the task requires. The engagement connects investigation to implementation, with a baseline, reviewed changes and clear acceptance criteria.
What’s included.
Three steps.
Understand
Agree the task, baseline, source systems and delivery boundary.
Build
Develop and review the code, configuration and integrations in scope.
Verify
Test representative cases, compare with the baseline and hand over.
Recognise the challenge?
Start with the problem you see. Explore what we can change, then open the implementation notes for the detail.
01AI cost-and-latency reduction engineering
What you may be seeing
- Our AI assistant takes too long to answer.
- AI API charges are rising rapidly.
- We send the same documents with every request.
- Simple questions use the same expensive model as difficult ones.
- Cheaper settings made answers inaccurate or incomplete.
We identify which parts of an AI task consume time and budget, then reduce unnecessary work without accepting a lower standard of answers.
Implementation & verification
We measure retrieval time, model time, token use and repeated calls for each supported AI task. We reduce unnecessary context, retrieve only relevant evidence and cache stable prompt prefixes or reusable results with correct access boundaries. We add task-based model routing, bounded tool calls and streaming where it improves perceived responsiveness. We compare each change against a representative quality evaluation set before accepting lower cost or latency.
How we verify the work
- Split slow requests into retrieval, tool calls, model startup and generation time.
- Benchmark task classes against quality thresholds before choosing a smaller model.
- Cache only compatible stable context; prevent one account's answer from being reused for another.
- Accept on task correctness, groundedness, cost per resolved task and time to useful response.
Tools considered for this work
Delivery: AI/backend development + provider configuration + evaluations. Model option: Claude API (a controlled connection between applications) with supported prompt caching; comparable provider features where available. Retrieval: existing search/vector store, such as Elasticsearch or Qdrant. Telemetry/evaluation: Langfuse or existing traces; custom Python quality/cost checks. Implementation: routing, context selection, caching, streaming and retry budgets.
Keep quality in the equation.
We test model selection, evidence retrieval and repeated work against representative questions. A faster or cheaper response is accepted only when it meets the agreed quality and task-completion criteria.
Explore the technical toolkit
Selected around your existing stack, licences, permissions and agreed scope.
Define what better means.
Speed
Loading, interaction and slow-request response times.
Reliability
Successful transactions and recovery under realistic load.
Unit cost
Spend per completed action, with traffic and quality considered.
Targets follow the baseline. These are measurement areas, not promised results.
Engineering terms, explained
- Grounded AI / RAG
- An assistant retrieves relevant approved evidence before generating an answer. Access controls and evaluations still need to be engineered and tested.
- Idempotency
- Retrying the same operation does not create an extra order, payment or record.
- Controlled experiment
- A concurrent comparison of assigned variants, with a defined outcome, observation window and decision rule.
- Core Web Vitals
- Measures of main-content loading, responsiveness to interaction and visual stability.
Technical references: Web Vitals · Azure document access · Structured data guidelines.
Quick answers.
Is this more than a website speed audit?
Yes. We work across the frontend, backend, database, cloud and AI workload. The diagnosis determines which layers need development; the engagement can include implementation and ongoing monitoring.
Can you improve our Azure environment?
We can assess Azure application performance, workload costs, Functions, database usage and monitoring. We agree the relevant services, access and implementation boundaries before work starts.
Will you guarantee a savings percentage?
No. We establish your baseline first and present evidence-backed opportunities. Any saving is checked against reliability, customer experience and output quality.