Your workflow. Your AI application.
Turn a language model into a dependable application with purpose-built interfaces, controlled context and business integrations. We select the smallest practical solution that meets your evaluation and deployment needs.
Built around the work that matters.
An off-the-shelf chat interface rarely matches the permissions, audit trail or workflow of a specialized business. Custom development lets you define what users can provide, what the model can access and how generated output becomes usable work.
The capabilities behind the experience.
A focused system with explicit responsibilities, useful interfaces and a maintainable implementation.
Application architecture
Separate model adapters, prompts, retrieval and business rules so individual components can evolve without rewriting the product.
Model selection
Evaluate hosted and deployable models on representative tasks. Consider operational burden and hardware alongside model quality.
Domain adaptation
Use retrieval and prompt configuration first. Explore fine-tuning only when suitable training examples and a measurable benefit justify it.
Usage controls
Set token budgets, per-user limits, timeouts and abuse controls. Give operators visibility into expensive and unreliable requests.
Understand how the parts connect.
MODELS → SYSTEMS
Interface
Capture a bounded task through a product interface with clear loading, correction and failure states.
When this approach makes sense
“Custom” does not always mean training a foundation model. Usually the distinct value lives in your data, evaluation criteria, tools and interface. We make those responsibilities explicit before estimating infrastructure.
Final technology choices follow discovery, data requirements and the deployment environment.
Start with a concrete use case.
Explore where this capability could fit into your operation.
- 01 / APPLICATIONA role-aware internal writing assistant.
- 02 / APPLICATIONA specialized document comparison tool.
- 03 / APPLICATIONAn AI feature embedded in an existing subscription product.
Clear decisions. Reviewable progress.
We begin with your current workflow, representative inputs and the people responsible for the result. Together we define the first useful release, success criteria and dependencies such as API access, data preparation or external approval.
Architecture and prototyping address the uncertain parts before we commit to the full implementation. During development, we review complete user journeys with you and test both successful operation and expected failures.
The handover includes the agreed source, configuration and operating documentation. Deployment, ownership, third-party costs and ongoing support are made explicit in the project scope.
The full delivery processDesigned for the real environment.
Access and information
We identify what information the system needs and who is allowed to use it. Credentials stay on the server, permissions are enforced at the data boundary and sensitive inputs are kept out of routine logs. Provider access and retention behavior are assessed against your requirements before deployment.
Reliability and growth
We define expected load and failure conditions rather than promising unlimited scale. Timeouts, controlled retries, database constraints and observable job status make errors recoverable. Backups and rollback procedures belong in the delivery plan, alongside the code.
Before the build.
01What should we bring to a custom llm applications discussion?
Bring the current workflow, a few representative inputs, your existing systems and the result you want to improve. “Custom” does not always mean training a foundation model. Usually the distinct value lives in your data, evaluation criteria, tools and interface. We make those responsibilities explicit before estimating infrastructure.
02Can you work with our existing software?
Yes. We first inspect the existing code, APIs, data and operational constraints. We preserve useful functionality and propose staged changes where a full replacement would add unnecessary risk.
03How do you handle private business data?
We agree what data can be used, which services may process it and who can access the result. The design can include scoped credentials, permission-aware retrieval, data minimization, retention rules and audit logs. The final controls depend on your requirements and selected providers.
04How long will our project take?
The schedule depends on scope, integrations, data readiness and acceptance requirements. After discovery, we propose milestones and identify external dependencies. We do not promise a fixed timeline before understanding the work.
05Who owns the code and what happens after launch?
Ownership, licensing and handover are agreed in the project contract. A handover can include source code, deployment instructions and operating documentation. Support and ongoing improvement are scoped separately so responsibilities are clear.
06How is a project priced?
We estimate from the agreed scope, complexity, integrations and delivery approach. Third-party usage and infrastructure costs are identified separately. The project planner provides a brief to discuss, not a binding quote.
The connected capabilities.
Let’s make intelligence useful.
Bring your custom llm applications requirements. We’ll define the next practical step.
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