Decisionware

The decision layer that determines what gets done.

Framework encodes your team's expertise into accurate, accountable AI decision models — automating the repeatable work so people focus on what only they can do.

See your business as Decisionware → See the Ericsson case Talk to us
The problem

Knowledge workers spend 60% of their time navigating the work — not doing it.

It's repeatable, learnable logic dressed up as judgment. Today, AI augments that work. It doesn't decide it.

01 / Search

Searching for information

Hunting for what already exists across tools, documents, and inboxes.

02 / Coordinate

Coordinating work

Chasing approvals and aligning teams instead of moving the work forward.

03 / Interpret

Interpreting policies

Manually applying compliance rules and policy logic to every situation.

04 / Route

Routing decisions

Figuring out who decides — and who to escalate to when they don't.

The approach

We compress the layers above the data into a single decision layer.

Most AI systems stop at insight. Framework encodes your knowledge workers' actual reasoning — with the accuracy and accountability required to decide.

L7
Decision
What do I do right now? — Framework's encoded Decisionware decides, not just augments.
L6
Planning
What's the sequence of actions?
L5
Reasoning
What are the correct next steps?
L4
Knowledge
What are the business requirements?
L3
Representation
What does the data tell me?
L2
Raw data
Where did the data come from?
Enterprise proof

Ericsson, at global scale.

In production
97%
Decision accuracy
Automated decisions consistently match knowledge worker outcomes.
90%
Cost compression
Per-decision cost cut from $56 to $6 across the workflow.
97.5×
Fewer human touches
Manual intervention nearly eliminated end-to-end.
$1.45M
Annual savings
Realized impact on a single sourcing workflow, year one.

The problem

A 28-person global sourcing team managing 15,000 transactions a year — growing 35% YoY — averaged 15 days per resolution. Labor-intensive, error-prone, and unscalable without proportional headcount.

The solution

Framework fine-tuned decision models on Ericsson's operational data, sourcing policies, and knowledge worker logic. The system now runs the full transactional workflow — SAP integration, contract parsing, procurement rules, decision awarding — with humans repurposed onto higher-value work.

Why it works

Built as a decision layer on top of what you already have.

Not every computation needs an LLM. Framework routes intelligently between small, accurate, reusable models — and the cost curve gets better as you automate more.

For-purpose architecture

  • Decision layer sits on top of existing systems — no rip-and-replace.
  • Models run on CPU instead of GPU using pre-processed data.
  • Reusable components compose into repeatable frameworks across workflows.

Best-in-class unit economics

  • Small, accurate, reusable models keep inference cost low.
  • Fewer calls and faster execution lower marginal cost.
  • Cost decreases per decision automated — opposite of LLM pricing curves.

Compounding accuracy

  • Most AI systems learn statistically — Framework learns structurally.
  • Structure compounds accuracy as decisions accumulate.
  • Each automated decision widens the moat against generalized solutions.
The team

From the operators who proved AI's material value at enterprise scale.

Framework is led by the team that put agentic automation into production at global enterprise scale.

Ericsson Amazon DataRobot Fox AppsFlyer Openmind Aduna 3 Mobile

Decide what gets automated.

If you've got repeatable, policy-driven decisions and a knowledge-worker bottleneck, we should talk.

hello@useframework.ai →