AI & Automation
LLM Integration that holds up under real users.
We focus on summarization, classification, or generation inside your app. Written scope, your stack, weekly demos — no account-manager layer.
The board slide says AI but llm integration. Most teams over-scope it. We write acceptance criteria first, then ship in 4-6 weeks for customer-facing AI with evals with OpenAI in your repo — not a parallel codebase that rots.
If you can't eval it with 50 real questions, it's not ready for customers.
We won't promise accuracy numbers without running evals on your docs.
Who this is for
- -Product leads adding llm integration with evals — not demo-day features.
- -Support or ops managers automating repeat work via llm integration.
- -Teams that tried a chatbot hackathon and need llm integration in production.
- -Founders who need llm integration scoped before the next fundraise narrative.
Problems we solve
- -LLM Integration estimates balloon because acceptance criteria were never written.
- -A previous vendor shipped llm integration that broke on edge cases in week two.
- -Your team lacks bandwidth to own llm integration while shipping the core product.
- -Integrations around OpenAI are fragile and nobody owns on-call.
- -Stakeholders disagree on what "llm integration done" means — so nothing ships.
What we deliver
- -Written scope for llm integration with explicit in/out of scope
- -Weekly demo — live or recorded — with decisions logged
- -Acceptance checklist signed before production launch
- -Runbook for the failure modes we expect in month one
- -Handoff doc so your team can maintain without us
- -Working implementation in your repo using OpenAI and Weaviate
How we work
- 1.Kickoff: access, repos, and 4-6 weeks for customer-facing AI with evals target
- 2.Prototype: rough end-to-end path for feedback early
- 3.Harden: edge cases, monitoring, and docs
- 4.Release: go-live support and next-step backlog
Why Futurebits
- -Stack-first: we start with OpenAI unless the audit says otherwise.
- -Direct access to the people writing code or design files.
- -We won't promise accuracy numbers without running evals on your docs.
Frequently asked questions
Who on your team works on LLM Integration?
The same small team from kickoff to launch — not a rotating bench. You talk to the people writing code or design files.
What does the first week of LLM Integration look like?
Access, repo setup, and a written scope draft. No build until you sign off on cut lines and the metric we're targeting.
Do you work with our existing OpenAI or Weaviate setup?
Yes, when it's sane. We audit first and tell you if something needs replacing — we won't rip out working infra for sport.
What if we already started LLM Integration in-house?
We pick up from current state, document what's there, and focus on what's blocking launch — not a rewrite unless necessary.
How is LLM Integration priced?
Fixed scope for sprints (4-6 weeks for customer-facing AI with evals). Broader work runs as a pod with weekly demos. We quote after a 30-minute scoping call.
Related services
AI SaaS Development
AI SaaS Development by Futurebits: AI features inside a SaaS product — not a separate demo app. One team from kickoff to launch — no hand-offs.
AI Product Development
AI Product Development by Futurebits: search, recommendations, or agents inside your existing product. Fixed window quoted after a 30-minute scoping call.
AI Workflow Automation
AI Workflow Automation by Futurebits: LLM steps in ops pipelines with human review where it matters. Ship in your stack with explicit cut lines up front.
Chatbot Development
Chatbot Development by Futurebits: website and support bots with escalation — not infinite intents. Direct access to the people doing the work.
SaaS Development
SaaS Development by Futurebits: billing, onboarding, and the first paid customer path. Ship in your stack with explicit cut lines up front.
MVP Development
MVP Development by Futurebits: one testable hypothesis — not a feature wish list. Acceptance tests signed before we call it done.