01
Multi-model approach
Match each task to a small, medium or large model; use frontier models only for reasoning-heavy steps.
70–80%
lower token and model cost
AI & GenAI · GenAI Ops & FinOps
Quality is non-negotiable. Cost is engineered.
GenAI spend grows unpredictably as use cases scale. We give you per-call visibility and apply proven levers — model routing, SLMs, context trimming, caching and committed capacity — without compromising quality.
The problem
GenAI costs creep up quietly.
01
Unpredictable spend
Costs grow with every policy, claim, call and conversation as use cases scale.
02
Expensive models on simple steps
Classification and extraction routed through frontier models by default.
03
No per-call visibility
Without observability, over-spend is found late — on the invoice.
Seven levers
Proven techniques to cut GenAI cost.
02
Observability
Track tokens, latency, model and success rate per call, user and flow — the prerequisite for every other lever.
Full
visibility of cost per use case
03
Provisioned throughput
Committed capacity (PTU) for stable, high-volume production workloads.
30–40%
cheaper than pay-per-use
04
Small language models
Fine-tuned SLMs for extraction, classification, tagging and validation.
3–10×
lower unit cost on narrow tasks
05
Context optimisation
Send only relevant chunks and summaries, not full documents and histories.
~8.5k→1.4k
tokens per prompt in a typical example
06
Caching & reuse
Response, embedding, retrieval and intermediate-output caches with safe invalidation.
Repeat
work never paid for twice
07
Deterministic pre/post-processing
Rules, regex, SQL and code for parsing, validation and routing; LLMs only for ambiguity.
Fewer
LLM calls, more reliable output
The architecture we build
Cheap to expensive, only when needed.
STEP 1
Rules / code first
Cheapest path
STEP 2
Small model
SLM / fine-tuned
STEP 3
Medium model
When the SLM falls short
STEP 4
Large model
Only when needed
STEP 5
Cache outputs
Reuse next time
STEP 6
Monitor
Tokens · cost · quality
Start cheap and simple. Escalate only when required. Measure everything. Reuse wherever possible.
Our offering
Assess, optimise, govern.
For enterprise LLM usage across Claude, AWS Bedrock, Azure OpenAI and open models.
Feature
Benefit
Cost assessment
Per-call, per-user and per-flow view of today's tokens, latency, models and spend.
Know exactly where GenAI money goes, and which steps are over-served.
Optimisation sprint
Apply the right levers: routing, SLMs, context trimming, caching, PTU.
Lower cost per use case with quality held fixed by evaluation.
Token attribution & dashboards
Cost per use case, team and user, with budgets and alerts.
Spend tracks value; finance and business owners see the same numbers.
Ongoing GenAI FinOps
Continuous monitoring as models, prices and usage change.
Savings that last, and new use cases that start cost-efficient.
What is your GenAI bill really paying for? Let’s find out.
Start with a cost assessment of one use case or your whole enterprise LLM estate.