AI & Automation
AI-Based Data Extraction in your repo, with weekly demos.
We focus on unstructured inputs turned into JSON your systems can ingest. Written scope, your stack, weekly demos — no account-manager layer.
Leadership wants AI in the product but ai-based data extraction. Most teams over-scope it. We write acceptance criteria first, then ship in 8-10 weeks when multiple systems need integration with Anthropic in your repo — not a parallel codebase that rots.
Demos that skip escalation paths fail the first week in production.
We won't train on customer data without explicit access boundaries.
Who this is for
- -Support or ops managers automating repeat work via ai-based data extraction.
- -Teams that tried a chatbot hackathon and need ai-based data extraction in production.
- -Founders who need ai-based data extraction scoped before the next fundraise narrative.
- -Product leads adding ai-based data extraction with evals — not demo-day features.
Problems we solve
- -AI-Based Data Extraction estimates balloon because acceptance criteria were never written.
- -A previous vendor shipped ai-based data extraction that broke on edge cases in week two.
- -Your team lacks bandwidth to own ai-based data extraction while shipping the core product.
- -Integrations around Anthropic are fragile and nobody owns on-call.
- -Stakeholders disagree on what "ai-based data extraction done" means — so nothing ships.
What we deliver
- -Written scope for ai-based data extraction 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.Align: goals, constraints, and who signs off on ai-based data extraction
- 2.Cut: smallest version that proves value — write it down
- 3.Ship: incremental releases with review each week
- 4.Measure: check the metric we agreed on; iterate or close
Why Futurebits
- -Typical window: 8-10 weeks when multiple systems need integration — stated in writing before we start.
- -Weekly demos with written decisions — not status decks.
- -Stack-first: we start with Anthropic unless the audit says otherwise.
Frequently asked questions
What if we already started AI-Based Data Extraction 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-Based Data Extraction 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-Based Data Extraction launches?
Yes — maintenance sprints or a partner retainer. Many teams keep us for the next bottleneck once v1 is stable.
Who on your team works on AI-Based Data Extraction?
The same small team from kickoff to launch — not a rotating bench. You talk to the people writing code or design files.
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