Don't just buy the cheapest model. Manufacture it.
When Podar sees the same expensive task thousands of times a month, it distills that task into a tiny specialist model and makes it a new destination inside the router.
What podar micromodels means.
A Podar MicroModel is a small, task-specific language model — typically a few hundred million to a few billion parameters — fine-tuned from an open-weight base on one narrow, measurable workload such as entity extraction, intent classification, or prose-to-JSON conversion. Podar does not distill general intelligence; it distills tasks. Each MicroModel becomes a new routing destination that serves the work at a fraction of frontier cost, with confidence and quality checks that escalate anything uncertain to a stronger model.
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Enterprises repeatedly pay frontier prices for narrow, repetitive work: extracting ten fields from an invoice, tagging a ticket, reshaping prose into JSON.
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Routing alone will commoditize — gateways, clouds, and model providers can all build a router.
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Nobody else has the telemetry showing which expensive requests keep going to frontier models unnecessarily, and why cheaper models failed.
Step by step.
- 01
Observe
Podar records the original and trimmed request, the model chosen, cost, evaluation result, escalations, which cheaper models failed, and why.
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Cluster
High-volume, high-cost task families are identified — for example 600,000 monthly insurance-document extractions on a frontier model.
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Build a teacher dataset
Licensed teacher models, customer-owned labels, and human-reviewed production outcomes generate training pairs with full data lineage.
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Fine-tune
An open-weight base model is tuned with supervised fine-tuning, LoRA, or QLoRA. Podar does not pretrain from zero.
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Shadow-test
The candidate runs silently beside the live route, compared on accuracy, format compliance, hallucination rate, latency, cost, human acceptance, and failure categories.
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Certify
Activation requires a customer-defined threshold — for example 99.5% structured-field accuracy with 100% escalation below the approved confidence boundary.
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Route
The MicroModel becomes the first destination for that task family, ahead of any external model.
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Escalate and retrain
Uncertain or failed requests go to a stronger model, and those failures become the next training curriculum.
What you get.
- Order-of-magnitude cost reduction on recurring, measurable workloads — not a percentage trim
- A published break-even calculation and Distillation Opportunity Score before any model is built
- Tenant-isolated private models by default; industry models only with explicit consent
Podar MicroModels — frequently asked.
- What is a Podar MicroModel?
- A Podar MicroModel is a tiny, task-specific model fine-tuned from an open-weight base on one narrow, measurable workload — extraction, classification, JSON transformation, PII detection, short summarization. It becomes a new destination inside the Podar router, with confidence checks that escalate anything uncertain to a larger model.
- Is Podar building its own general-purpose model?
- No. Podar will not attempt to distill a general-purpose mini model. It builds a growing portfolio of narrow specialists that are certifiably good enough for one task family each.
- Which tasks are good distillation candidates?
- Excellent candidates: intent classification, entity extraction, prose-to-JSON, document categorization, PII redaction. Strong: standard support answers, policy matching, short domain summarization, text-to-SQL on a stable schema. Poor: open-ended strategy, complex multidisciplinary reasoning, novel legal or medical judgment, long-horizon agents, and current-events answers without retrieval.
- How do you handle the legal side of distillation?
- With a teacher-rights policy. Major providers' terms restrict using their outputs to train competing models, so Podar trains on open-weight models with permissive licenses, customer-owned labeled data, human-reviewed outcomes, negotiated distillation rights, or provider-hosted fine-tuning — and keeps data lineage for every training example.
- Could a MicroModel be retired?
- Yes. Podar stays economically neutral even toward its own models. If an external model becomes cheaper, or a task changes materially, traffic is reallocated or the MicroModel is retired.
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