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Prompts, tools and parsing get written for one vendor. Months later, changing models means touching everything.
Prompts and formats are tied to one vendor.
Simple tasks use the same expensive model as hard ones.
Figures produced by a language model are hard to verify.
Some data must stay in specific environments.
We define interfaces and tests for each task, then route work to approved models based on measured quality, speed, cost and policy.
Separate users, presentation, interfaces, business logic, data, models and infrastructure, with one job per layer.
Send each task to the model that meets its bar, and keep calculations and rules in code.
Keep provider-specific prompts and formats behind stable contracts so alternatives can be tested.
Keep sensitive work, including data rooms, in approved environments with access controls.
Test a replacement model on real tasks for accuracy, failures, speed and cost before switching.
We find the models, tools, data and policies that are tightly coupled.
We define task contracts, routing rules and access boundaries.
We swap one workload in a controlled test and compare the results.
Models are judged on your tasks, not vendor claims.
Expensive models handle only hard work.
Calculations run in code.
Models change behind stable interfaces.
Work goes only to approved environments.
Changes are tested before customers see them.
Fallbacks and review points limit damage.
You always have a working alternative.
Client engagement, anonymized
A large private equity firm wanted AI across its deal process without vendor lock-in, invented figures or risk to data room confidentiality. We designed an eight-layer architecture in which the model occupies one layer, business logic runs on MCP servers and every financial figure is calculated in code. The design lets the firm change models task by task and swap the presentation layer without touching the logic beneath it.
A design that keeps business logic, data and tools independent of any one AI provider, using stable interfaces so models can be swapped without rewriting the application.
Put an interface between your application and the model, keep prompts and tool definitions provider-neutral, store tests for every task and route through a layer you control.
No. Language models can produce plausible but wrong numbers. Keep calculations and rules in code and use the model for language tasks.
Set a quality bar, test candidates on real examples and pick the cheapest one that passes. Harder cases go to a stronger model or a person.
Yes. Routing rules send sensitive work only to models in approved environments, including privately hosted ones.
We will map the dependencies and test one swap against your quality, security and cost needs.
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