AI assistants
Support bots and internal copilots that answer from your knowledge base, call your tools and hand off to humans when they should.
Ship AI Features That Move the Needle
LLM integration, AI chatbots, RAG systems, and AI-powered features — built by engineers who have shipped real AI products.
- LLM features, RAG and agents running in production
- Evals, guardrails and observability from day one
- Model-agnostic: OpenAI, Anthropic, Gemini and more
Trusted by startups worldwide
AI & LLM development
From copilots to production-grade RAG
AI-powered workflows
Document extraction, classification and agents that take real actions — measured against evals so quality stays predictable.
Chatbots & copilots
Assistants grounded in your data with citations and safe fallbacks.
RAG & knowledge bases
Chunking, embeddings and retrieval tuned for accuracy on your documents.
Document processing
Extract structured data from PDFs, emails and forms at scale.
Evals & monitoring
Test sets, quality scores and tracing so regressions are caught early.
Guardrails & privacy
PII handling, prompt-injection defences and scoped tool access.
Cost & latency tuning
Model routing, caching and streaming that keep responses fast and affordable.
How it works
How does a Brimode AI integration work?
We start with the job to be done and the data behind it, prove quality, latency and cost in a short spike, then build the feature with evals and guardrails. Releases go out behind feature flags with tracing so you can see exactly how the model behaves.
We integrate OpenAI, Anthropic, and Vertex models into your product; build support and internal assistants; ship RAG over your documents; automate document extraction; and design evals, guardrails, and observability so teams trust the output.
Book a discovery call01 · 24–48H
Use Case & Data Audit
Define the job-to-be-done, data sources, and success metrics. Architecture for models, retrieval, and API boundaries.
02 · 2–4 DAYS
Model & Integration Spike
Prove latency, cost, and quality in code — eval harness, prompt baselines, and API stubs before full feature build.
03 · 1–4 WEEKS
Build Pipelines & APIs
RAG, agents, guarded routes, and admin tooling in sprint rhythm. Same PR review and staging deploys as product engineering.
04 · 3–5 DAYS
Evals & Guardrails
Accuracy, safety, and cost thresholds on staging. Feature flags, logging, and rollback hooks before user-facing rollout.
05 · 24–48H
Staged Rollout
Progressive release with monitoring on tokens, errors, and drift. Production config and ops runbooks included.
06 · 48H TURNS
Monitor & Tune
Prompt updates, new intents, and infra tuning in 48-hour turns. Eval dashboards stay green as usage grows.
Dedicated support
Senior support for every AI feature
You work directly with the engineers writing your code — async-first, human when it matters.
Senior team, every sprint
A lead engineer owns your project end to end — architecture, estimates and code review — not a revolving door of juniors.
Book a discovery call4-hour response target
Questions, bugs and feedback get a real answer fast, in Slack or email, with async Loom updates between calls.
Email the teamWeekly demos, no surprises
You see working software on staging every week, with a written scope before code and a flat monthly price.
Start a projectEngineering standards
Quality at every release
The same delivery habits on every sprint, so your AI feature stays fast, secure and easy to change.
Written scope
Acceptance criteria agreed before any code is written.
Code review
Every pull request reviewed by a senior engineer.
CI/CD pipelines
Automated builds, tests and preview deploys on every change.
Security baked in
Secrets management, least-privilege access and dependency scanning.
Monitoring & rollback
Alerts, logs and a tested rollback path for every release.
Handover docs
Decisions and runbooks your next hire can pick up.
Tech & integrations
Works with the tools you already use
Partners
OpenAIAnthropicGoogle Vertex AILangChainLlamaIndexPineconeWeaviateChromaDBAWS BedrockHugging Face
AI & LLM Development FAQs
Which AI models do you work with?
OpenAI, Anthropic, Google Gemini, AWS Bedrock and open-source models. We choose per use case on quality, cost and latency.
Is our data used to train models?
No. We use API tiers and settings that keep your data out of training, and scope access to what each feature needs.
How do you stop hallucinations?
Retrieval grounding, citations, structured outputs and eval suites that block releases when quality drops.
Can you add AI to an existing product?
Yes. Most projects add AI features to live products behind tests and staging review.
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Let's build something your users will love.
Book a free 20-minute discovery call. No pitch deck needed.
2–4 days
Typical sprint length
4 hours
Response target
2 hubs
London & Johannesburg