Cut inference costs by up to 70 percent without cutting output quality.
THE CHALLENGE
AI systems tend to get expensive quietly. A prompt that grew over six months, a context window packed with material the model never uses, and every request routed to the most capable and most expensive model available. The bill arrives long after the architectural decisions that caused it.
OUR APPROACH
Reduce inference costs by up to 70% through intelligent prompt compression, caching strategies, context management, and model routing architectures.
We understand your business requirements, existing systems and operational challenges.
We design an AI approach that fits your workflows, data and technology environment.
We turn the solution into a production-ready system that can be measured and improved.
WHAT'S INCLUDED
A practical engagement designed around your business requirements, technical environment and desired outcomes.
Audit token usage and costs across your AI workloads.
Reduce unnecessary prompt content while preserving useful context.
Introduce caching strategies to reduce repeated inference.
Optimize how context is selected, managed and retrieved.
Route simpler requests to more cost-effective models where appropriate.
Optimize workload execution through appropriate batching and streaming approaches.
Measure savings while ensuring quality does not fall below the agreed benchmark.
Track costs and attribute spend to individual features.
HOW WE WORK
A structured process keeps every engagement focused, transparent and aligned with business outcomes.
Talk to our teamAudit your data, tech stack, and AI readiness
Map high-value use cases to business priorities
Architect the solution with security-first principles
Build iteratively with continuous stakeholder input
Monitor, fine-tune, and scale for sustained ROI
EXPECTED OUTCOMES
We focus on outcomes that create practical value for your organization, rather than implementing technology for its own sake.
TECHNOLOGY & PLATFORMS
We choose technologies based on your requirements, infrastructure, scalability and long-term maintainability.
LLM
LLM
Open Source
Open Source
LLM
Framework
Framework
Platform
ML
Cloud
Cloud
Cloud
Infra
Infra
Vector DB
FAQ
Everything you need to know before starting an engagement with Nitiverk.
Ask our teamQuality regression testing is used to compare optimized workloads against the pre-optimization benchmark.
The achievable saving depends on the workload and current architecture. A baseline audit is used to determine the opportunity.
The timeline depends on the number of workloads, current architecture and optimization opportunities identified.
Optimization can be applied to your existing AI stack rather than requiring a complete replacement.
The work can be structured as a focused optimization engagement or continued monitoring and optimization.
The baseline is established by measuring current token usage, workload behavior and inference costs before optimization.
EXPLORE MORE
End-to-end delivery of production-grade AI products: from architecture design and model selection through to deployment, monitoring, and iteration.
Deploy powerful open-source language models within your private infrastructure. Full data sovereignty, air-gapped environments, and compliance-ready.
AWS, Azure, GCP — we architect cloud-agnostic solutions that avoid vendor lock-in and optimize cost.
Whether you are exploring a new AI opportunity or scaling an existing system, let's discuss how Nitiverk can help you move from strategy to production.