No — the eval harness is the gate. We don't downsize a model until the eval shows the smaller model meets or beats the larger one on your real data. If a use case can't be downsized, we leave it alone.

Which providers do you work with?

OpenAI, Anthropic, AWS Bedrock, Google Vertex, Azure OpenAI, and open-source via vLLM or together.ai. We're agnostic — the routing layer can mix providers, and many of our biggest savings come from running the right model on the right provider.

How quickly do we see savings?

Typically a 20–30% cut by week 3 from prompt caching alone — that's the lowest-hanging fruit and often the single biggest lever. Full 40–70% reduction usually lands by week 6–8 once routing and eval-verified downsizing are deployed.

Is this a one-time engagement or ongoing?

Either. Most clients take a fixed-fee implementation, then a quarterly check-in to catch creep — new features, new models, new prompts that drift. LLM cost is a living target; one cleanup doesn't last forever.

LLM Cost & Latency Optimization

LLM Cost Optimization — Cut Your AI Bill 40–70%

If your OpenAI or Anthropic bill is rising faster than your usage, you're paying for waste. We audit your LLM spend, route requests to the right-sized model, cache aggressively, and tune prompts — typically cutting bills 40–70% without dropping accuracy.

Where the savings come from

≈ 55% bill reduction (typical)

Composite from recent audits — exact mix varies by workload.

Prompt caching — 35%: Anthropic / OpenAI / Bedrock cache for system prompts and tool definitions.
Model right-sizing — 30%: Route easy 80% to Haiku / 4o-mini after eval-verified switch.
Prompt diet & compression — 20%: Trim system prompts, drop unused tool defs, compress context.
Streaming + batching — 10%: Use streaming where UX matters, batch where it doesn't.
Retrieval cleanup — 5%: Smaller chunks, smarter k, less context bloat.

Numbers are typical, not a quote. The audit gives you the real projection for your workload.

What you get

Spend audit across every model, prompt, and feature — most clients can't see this clearly today

Eval harness that proves a smaller/cheaper model is good enough for each use case before we switch

Model routing layer: GPT-4 for hard requests, GPT-4o-mini or Haiku for the 80% that don't need it

Prompt cache strategy (Anthropic prompt caching, OpenAI cache, Bedrock cache) — biggest single lever for many teams

Prompt compression and template refactoring — pricing is per-token, so token diet pays back instantly

Latency profile: p50/p95 reduction, streaming where it matters, batching where it doesn't

Monthly cost dashboard so the savings stay saved instead of drifting back up

When it fits

Monthly LLM spend is already painful ($20k+/mo) or growing fast
Latency is a real product issue — users wait, conversion drops, on-call gets paged
You have evaluation signal (or are willing to create it) so we can downsize models with confidence
Someone owns the bill — finance, engineering leadership, or the platform team

When it doesn't

The spend is genuinely small — premature optimization will cost more than it saves
You can't measure quality, so we can't prove the cheaper model is good enough
The accuracy floor is regulatory — sometimes the expensive model is required and we'll tell you

Process

Week 1: audit — spend by model, feature, and prompt; eval-coverage check. Weeks 2–3: build the eval harness for the top 3–5 use cases. Weeks 4–6: implement model routing, caching, and prompt diet behind feature flags. Week 7: rollout and dashboard handover. Most clients see the first ~25% cut by week 3.

Full delivery process

Pricing

Fixed-fee audit ($10–20k) — produces savings projection and is creditable against implementation. Implementation runs $40–120k depending on surface area. Outcome-based pricing available when current spend is high enough that the math works for both sides.

See engagement models

Case studies

Human Resources & Recruitment

AI-Powered Applicant Tracking System

Comprehensive ATS solution with AI-driven candidate matching, automated resume parsing, and real-time recruiter-candidate communication serving 10K+ monthly candidates.

FAQ

Will accuracy drop?: No — the eval harness is the gate. We don't downsize a model until the eval shows the smaller model meets or beats the larger one on your real data. If a use case can't be downsized, we leave it alone.
Which providers do you work with?: OpenAI, Anthropic, AWS Bedrock, Google Vertex, Azure OpenAI, and open-source via vLLM or together.ai. We're agnostic — the routing layer can mix providers, and many of our biggest savings come from running the right model on the right provider.
How quickly do we see savings?: Typically a 20–30% cut by week 3 from prompt caching alone — that's the lowest-hanging fruit and often the single biggest lever. Full 40–70% reduction usually lands by week 6–8 once routing and eval-verified downsizing are deployed.
Is this a one-time engagement or ongoing?: Either. Most clients take a fixed-fee implementation, then a quarterly check-in to catch creep — new features, new models, new prompts that drift. LLM cost is a living target; one cleanup doesn't last forever.

Ready to talk llm cost & latency optimization?

30-minute scoping call. No obligation, no hard sell.