ANNA OS powered ASAMIA AI · Intelligence

Reasoning model selection

Match complex decisions to intelligence that can work through the evidence while recognising when an answer needs review.

Documented capability

ASAMIA ANNA OS concept: a branching decision path passing review gates — ANNA OS · Concept illustration

Purpose and business value

Reasoning selection starts with the decision, not a generic intelligence score. A role may need to compare several policies, reconcile conflicting information or build a recommendation from a long document set. Evaluation should test whether the result follows the supplied rules, distinguishes missing information and supports its recommendation with inspectable evidence.

Task-based selection aims to improve decision support without spending the same resources on every routine enquiry.

Business use cases

Invoice exception review

Compare an invoice, purchase order and receipt; draft the mismatch explanation for a finance reviewer.

Policy-led triage

Assess a request against supplied eligibility rules and flag ambiguous cases instead of inventing an exception.

Executive synthesis

Bring together authorised reports, highlight conflicting assumptions and prepare a decision brief with source references.

Technical requirements

01
Representative multi-step tasks with expected decisions
02
Source-grounding, uncertainty and escalation checks
03
Response-time and cost limits for the intended workflow
04
Approved model modality and endpoint access

Functional specification

Compare configurations using the same task set. Record rule adherence, evidence quality, error patterns and reviewer corrections; define when a simpler path or human decision is required.

Operating controls

Selection is documented for deployment planning. These pages do not switch the intelligence used by live BuildLab recommendations.

Model choice & ANNA LLM