AI Services
LLM Feature Development
Production LLM features for SaaS products, internal platforms, research tools, and customer workflows.
What's Getting in the Way
Adding an LLM feature is easy as a demo and hard as a product. The real work is product design, data flow, model selection, latency, cost, security, evaluation, fallbacks, and integration with the rest of the application.
- The prototype works, but it is too slow, expensive, brittle, or inconsistent for production
- The feature needs to connect to product data, permissions, user context, and existing workflows
- Engineering teams need AI-specific architecture without turning the app into a science project
- There is no evaluation loop to know whether changes improve the feature
What You Get
Feature Scope and Product Flow
We define the user workflow, input/output behavior, edge cases, permissions, and success criteria.
LLM Architecture
We design the model calls, prompts, data retrieval, caching, routing, fallbacks, and cost controls.
Feature Implementation
We build the AI feature into your product or internal tool using your existing stack and APIs.
Evaluation and QA
We test output quality, latency, cost, failure cases, and regressions before launch.
Documentation and Handoff
Your team gets implementation notes, operating guidance, and clear ownership of the feature.
The Process
Shape the Feature
We clarify the user problem, product surface, data access, constraints, and launch criteria.
Design the System
We define the LLM workflow, prompts, retrieval, model selection, observability, and fallback behavior.
Build the Feature
We implement the feature, connect it to your product, and keep the user experience clean.
Evaluate and Ship
We test with realistic examples, fix weak outputs, document the system, and prepare for production.
Pricing
$6K-$18K
LLM Feature Development
Timeline: 3-6 weeks
Final price depends on scope. We confirm exact pricing after a free scoping call.
Scope your LLM featureQuestions About This Service
What kinds of LLM features do you build?
Summarization, extraction, classification, semantic search, copilots, document workflows, recommendations, chat interfaces, and research tools.
Can you work inside our existing codebase?
Yes. We can build alongside your team or deliver the feature in a separate service depending on the stack and access model.
Do we need OpenAI specifically?
No. We choose models based on quality, cost, speed, privacy, and deployment needs.
How do you keep costs under control?
We use model selection, routing, caching, prompt design, batching, and evaluation to control unnecessary usage.
Can you help after launch?
Yes. We can support iteration, evaluation, monitoring, and optimization after users start using the feature.
Often Combined With
Let's build it.
Tell us your context. We'll scope it in one call.