Your generative AI prototype impressed a demo audience but falls apart on real user inputs.
Service
Generative AI products, built for real users, not demos.
We design and build LLM-powered features — copilots, chat assistants, content generators — engineered for accuracy, cost, and latency in production, not just a proof of concept.
Build flowActive
- Discovery & use-case scopingDefine the job the model must do
- 02Model & architecture selectionFoundation model, RAG, or fine-tune
- 03Prototype & evaluationTest accuracy before committing
- 04Production buildGuardrails, monitoring, human review
- 05Launch and iterateUsage data feeds the next version
Why teams come to us
What this service helps you solve
You don't know which foundation model, prompting strategy, or fine-tuning approach fits your use case.
Unchecked model output is creating hallucination, cost, or compliance risk.
You need generative features that stay accurate and affordable as usage scales.
Deliverables
What’s included
Discovery and use-case scoping sessionModel and architecture selection (foundation model, RAG, or fine-tuning)Prompt design and evaluation frameworkProduction build with guardrails and content filteringCost and latency optimizationHuman review checkpoints for model outputMonitoring and usage analytics setupStatement of work before billing
Process
How the service works
- 01
Scope the specific job the generative feature must perform
- 02
Select the model and retrieval or fine-tuning strategy
- 03
Prototype and evaluate against real sample inputs
- 04
Build the production version with guardrails and monitoring
- 05
Launch, measure, and refine based on real usage
Roles
Roles that may support this service
AI EngineerMachine Learning EngineerBackend DeveloperPrompt EngineerQA Engineer
Final roles are recommended after discovery.Discovery before SOW
Discovery comes before every reliable statement of work.
Before we recommend roles, timelines, or pricing, we need to understand your goals, technology stack, product situation, scope, risks, and constraints. Discovery helps us align expectations and create a realistic statement of work.
What discovery documents
- Business goals
- Product goals
- Technology stack
- Current situation
- Required roles
- Timeline expectations
- Budget expectations
- Risks and unknowns
- Success criteria
FAQ
Questions about this service
We're model-agnostic and select based on your use case, budget, and data requirements, including options from OpenAI, Anthropic, and open-source models. Discovery determines the right fit rather than defaulting to one vendor.
We combine retrieval-augmented generation, evaluation datasets, and guardrails with human review checkpoints on model output. AI accelerates generation, but accuracy decisions stay with your team and ours.
Yes. We regularly take a proof of concept through evaluation, hardening, and a proper statement of work rather than starting from zero, as either an ongoing team or a fixed-scope project.