Language models wired into your product
Model selection, prompt and context design, caching and cost control — the engineering between a demo and a feature.
What you get
- Model selection and benchmarking
- Prompt and context architecture
- Response caching and streaming
- Fallback and rate-limit handling
- Token cost monitoring
What changes
- A provider outage degrades the feature instead of the product
- Cost per request is a number you can see and cap
- Answers stream instead of arriving after a long wait
When teams call us
- A working prototype that is too slow or too expensive to ship
- A feature that breaks whenever the provider rate-limits
- Choosing between models without guessing
Process
How the work runs
The same four phases whichever engagement model you pick, each ending in something you can read.
Feasibility
Whether a model genuinely beats deterministic code here, tested on your data before anyone commits a budget.
- Data review
- Baseline comparison
- Go/no-go in writing
Evaluation harness
Built before the feature, not after. Without a scoring set you cannot tell a prompt change from a regression.
- Golden test set
- Scoring criteria
- Regression runs
Build & tune
Retrieval, prompts, tools and guardrails developed against that harness, with cost and latency tracked per change.
- Versioned prompts
- Cost per request
- Latency budget
Ship & monitor
Live with logging of inputs, outputs and tool calls, so quality is observed rather than assumed.
- Output logging
- Drift alerts
- Spend caps
LLM Integration, specifically
The questions that come up on this work in particular.
Still have a question? Talk to the team
Not sure which service your project needs?
Most useful projects cross two or three of them. Describe the outcome and we will map it.