Classify 4,000 support tickets
A compact model tags and clusters the queue in minutes - a frontier model would be slower and cost far more NTUs.
The right model. Every single time.
Nimbus routes each job to the model that gets it done most efficiently. Tokenminning, not token maxxing.
Maximum outcome per token - not maximum spend.
Matching model size, context window, and modality to the actual task. Faster answers, lower NTU spend, and the same or better quality.
- Efficient NTUs
- Wasted NTUs
- NTU spend
Right-sized models for every class of work.
Time-series & numerical engines
Forecasting, anomaly detection, and demand planning - no language reasoning tax on problems that are really just math.
Compact language models
Fast, low-cost models for extraction, classification, tagging, and routine drafting.
Frontier reasoning models
Deep reasoning for strategy, planning, and complex synthesis - reserved for initiatives that genuinely need it.
Retrieval & embedding models
Ground answers in your Company Wiki and Lifecycle Graph so responses cite your context.
Multimodal models
Documents, tables, charts, and screenshots parsed by models built for the modality.
Hundreds of domain specialists
Every agent carries the specific models that fit the work it does.
Real examples from the field.
Forecast next quarter's cash
A numerical time-series engine runs the projection - not a language model asked to do arithmetic.
Model a market-entry strategy
A frontier reasoning model reads filings, memos, and past decisions together to reason across the whole picture.
Flag non-standard terms in vendor MSAs
Retrieval chunks each agreement; a compact model tags deviations against your playbook - not a frontier model re-reading hundreds of pages end to end.
Cluster friction themes from product telemetry
Embedding models group sessions by behaviour; a compact model names the themes - counts and trends stay in analytics, not an LLM guessing at numbers.
Score inbound leads before morning standup
A compact model extracts firmographics and intent from CRM notes overnight - frontier reasoning waits for complex multi-stakeholder deals.
Parse error screenshots from incident threads
A multimodal model reads stack traces and UI state from images - not a text-only model asked to infer what failed from a vague description.
Summarise account health for QBR prep
Retrieval pulls tickets, usage, and contract context; a compact model drafts the brief - frontier models only when exec escalation needs full synthesis.
Localise and tag 40 campaign variants
Compact models handle tone, length, and locale constraints at scale - frontier spend is reserved for net-new creative strategy, not every subject line.
Detect subscription revenue drift
A time-series engine spots anomalies across billing cohorts - not a language model narrating variance it cannot reliably compute.
Screen résumés against open requisitions
A compact model extracts skills and maps them to reqs in bulk - deeper culture-fit synthesis uses frontier models only on shortlisted finalists.
Map controls to evidence in audit packets
Retrieval grounds each control in wiki policies and ticket exports; a compact model links evidence - frontier reasoning is saved for gap analysis across frameworks.
