AI Agents & LLM Integration
LLM features that survive contact with real users.
Demos are easy; production is not. We design retrieval, prompts, and agent loops around an evaluation set first, then ship behind flags with tracing, cost caps, and clear fallbacks when the model should not answer.
What we do
- RAG and knowledge retrieval
- Multi-step agent orchestration
- Evals, prompts, and model selection
- LLM ops, logging, and cost controls
How it ships
Quality baselines first, ship behind flags, then iterate on eval datasets—not guesswork.
Good fit if
- Products adding search, chat, or summarization over their own data
- Teams whose prototype works in a notebook but fails on edge cases
- Companies that need agents to take actions, not just generate text
How we engage
Scoped, time-boxed steps so you can start small and expand once the first result is in.
- 01
LLM feasibility sprint
Two weeks. We build an eval set from your real data, test candidate models and retrieval strategies, and report what quality and cost to expect.
- 02
Production integration
Four to ten weeks. Retrieval pipeline, prompt and tool design, guardrails, tracing, and a staged rollout into your product.
- 03
Agent platform
Ongoing. Multi-step agents with tool use, memory, and human approval steps, operated with runbooks your team can follow.
Proof from our own products
We run this work in production for ourselves before we offer it to clients.
Zema Blue
Distill the noise into a persona
Rank persona corpora for your goal, compose composite agents, and chat with answers cited from sources.
Read the case study
FeedbackAI
AI hiring & assessments
Two-sided platform for job seekers and hiring teams—AI-scored matches, skill gaps, and pre-ranked shortlists.
Read the case studyQuestions we get about AI Agents & LLM
Which models do you work with?
OpenAI, Anthropic, Google, and open-weight models served locally or via providers. Model choice is an output of the evaluation, not an input.
How do you keep costs predictable?
Caching, routing cheap models for easy cases, token budgets per request, and dashboards that show spend per feature from day one.
Can you work with sensitive data?
Yes. We can keep retrieval and inference inside your cloud or on self-hosted models, and we design redaction into the pipeline where required.
Have a project like this?
Tell us what you are building. We reply within one business day with questions or a proposed next step.