AI INTEGRATION SPECIALISTS
Context Engineering & Token Optimization

Do More With Fewer Tokens

Lower cost, lower latency — same or better answers

We re-engineer how your system assembles context — what's retrieved, structured, cached and compressed — so you get the same quality, often better, at a fraction of the spend and latency.

0Fewer Tokens (typical)
LowerLatency Per Call
Same+Answer Quality
MeasuredBefore & After
The Tax

Tokens Are a Tax on Every Call

When context is sloppy, you pay for it on every request — and the bill scales with usage, not value. Worse, bloated context quietly degrades answer quality.

⚠️ Where the waste hides

  • Entire documents stuffed into context "just in case"
  • System prompts re-sent in full every call, never cached
  • Retrieval that returns 20 chunks when 3 would answer better
  • Multi-turn history that grows unbounded until it's mostly noise

✦ What engineered context looks like

  • Right-sized retrieval — only the chunks that actually answer
  • Prompt caching so static context isn't paid for twice
  • Compression of long histories without losing the signal
  • Model routing — cheap models for easy turns, strong where it counts
Capabilities

What We Optimize

Every lever that moves token spend and latency — measured against your real traffic, not a synthetic benchmark.

🔍

Retrieval Design

Chunking, embeddings, re-ranking and top-k tuning so the model sees the few passages that actually matter — not a haystack. The single biggest lever on both cost and answer quality.

RAGRe-rankingHybridtop-kEmbeddings
🧩

Prompt Structure

Restructured prompts that say more in fewer tokens, in the format and ordering models respond to best.

💾

Caching & Reuse

Prompt, embedding and response caching so repeated and static context stops costing you on every call.

🗜️

Context Compression

Summarization and distillation of long histories and documents that keep the signal and drop the filler.

🔀

Model Routing

Cheap, fast models for the easy turns; frontier models reserved for the calls that actually need them.

📈

Eval & Guardrails

An eval harness that proves quality held or improved while cost dropped — so optimization is never a guess.

The Method

From Bloat to Lean

A measured loop — we never trade away quality to chase a cheaper bill. Every change is proven against an eval set.

1

Baseline

Instrument real traffic to measure token spend, latency and quality today.

2

Profile

Find where tokens are wasted and where quality is actually won or lost.

3

Re-engineer

Rework retrieval, prompts, caching and routing against an eval set.

4

Prove

Show cost and latency down with quality held or improved — with numbers.

5

Hand Off

Document the architecture and dashboards so the gains stick.

Tooling

The Toolkit

Provider-agnostic techniques that apply whether you're on Claude, an open model, or a mix.

RAGRe-rankingPrompt cachingEmbeddingspgvectorClaudeRoutingEval harnessToken tracingCompressionHybrid searchMCP RAGRe-rankingPrompt cachingEmbeddingspgvectorClaudeRoutingEval harnessToken tracingCompressionHybrid searchMCP

Retrieval

RAGRe-rankingHybrid searchChunking

Prompting

Prompt cachingFew-shotStructured output

Models

ClaudeOpen modelsRoutingMCP

Vectors

EmbeddingspgvectorQdrant

Eval

Eval harnessRegression setsA/B

Observability

Token tracingLatencyCost dashboards
📉

Quality First, Then Cheaper

We never ship a "cheaper" pipeline that quietly got dumber. Every optimization is validated against an eval set built from your real cases — so we can prove the bill went down while the answers held or improved. If a change costs quality, it doesn't ship.

Stop Paying the Token Tax

Send us your current pipeline and a slice of real traffic. We'll show you where the tokens are going — and what they could be.