AI & Automation
AI Recommendation Systems without the six-month discovery phase.
We focus on ranking that uses your data — not generic collaborative filtering. Written scope, your stack, weekly demos — no account-manager layer.
Support is answering the same ai recommendation systems is blocking something else from shipping. We take a fixed window (8-10 weeks when multiple systems need integration), integrate with Anthropic and pgvector, and demo progress every week.
Demos that skip escalation paths fail the first week in production.
We won't replace your support team on day one — deflection comes in phases.
Who this is for
- -Support or ops managers automating repeat work via ai recommendation systems.
- -Teams that tried a chatbot hackathon and need ai recommendation systems in production.
- -Founders who need ai recommendation systems scoped before the next fundraise narrative.
- -Product leads adding ai recommendation systems with evals — not demo-day features.
Problems we solve
- -AI Recommendation Systems estimates balloon because acceptance criteria were never written.
- -A previous vendor shipped ai recommendation systems that broke on edge cases in week two.
- -Your team lacks bandwidth to own ai recommendation systems while shipping the core product.
- -Integrations around Anthropic are fragile and nobody owns on-call.
- -Stakeholders disagree on what "ai recommendation systems done" means — so nothing ships.
What we deliver
- -Written scope for ai recommendation systems 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 Anthropic and pgvector
How we work
- 1.Intake: stakeholders, Anthropic access, and success metric
- 2.Spec: written scope with in/out and test cases
- 3.Build: pair with your team or solo in your repo
- 4.Handoff: docs, runbook, and optional retainer
Why Futurebits
- -Direct access to the people writing code or design files.
- -We won't replace your support team on day one — deflection comes in phases.
- -Typical window: 8-10 weeks when multiple systems need integration — stated in writing before we start.
Frequently asked questions
Do you work with our existing Anthropic or pgvector 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 AI Recommendation Systems 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 AI Recommendation Systems priced?
Fixed scope for sprints (8-10 weeks when multiple systems need integration). Broader work runs as a pod with weekly demos. We quote after a 30-minute scoping call.
What do you need from us to start?
One decision-maker, repo or staging access, and honest constraints (timeline, budget, stack). Existing docs help but aren't required.
Can you stay on after AI Recommendation Systems launches?
Yes — maintenance sprints or a partner retainer. Many teams keep us for the next bottleneck once v1 is stable.
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