Research Initiative
A domain-specialized model that converts qualitative human evidence into structured, comparable, decision-ready intelligence — cost-efficient, auditable, and built for production. Not a frontier model wrapper. A dedicated system for the domains where decisions carry real weight.
Frontier models are impressive. But for high-stakes, evidence-heavy decisions, generic AI falls short in three ways.
Frontier models excel at general reasoning, but produce surface-level outputs in specialized domains. They don't understand domain rubrics, evaluation frameworks, or what "good" looks like in context.
Frontier model inference is expensive. High-value decisions need to be repeated many times — across many inputs, many scenarios, many iterations. Generic AI doesn't scale economically for production use.
High-stakes decisions require evidence traceability and explainable reasoning — not opaque prose. Generic models produce vague narratives; Strata produces structured, machine-consumable outputs.
Instead of competing with frontier models on breadth, build domain-specialized models with unique architecture and training. A narrow, optimized system that outperforms generic AI at the tasks that matter — and does it cheaply enough to run in production.
The Evidence-to-Intelligence Pipeline
Qualitative Evidence Ingestion
Raw inputs: notes, documents, feedback, assessments, comments — unstructured human evidence
Evidence Normalization
Convert messy inputs → structured evidence objects with competency, direction, strength, source
Rubric-Aware Domain Reasoning
Merge evidence with domain-specific rubrics and standards (org requirements, evaluation frameworks)
Structured Decision Intelligence
Output: comparable views, explicit reasoning, evidence traceability, recommendation + uncertainty
Hiring is the first domain we're proving Strata in. It's evidence-heavy, high-stakes, legally sensitive, and repeated constantly — ideal conditions for a specialized model to outperform generic AI.
Debrev Interview is where Strata meets real teams. Interview audio, notes, and resumes flow in. Structured candidate intelligence — competency signals, risk flags, evidence-linked insights — flows out and into product workflows.
Every real hiring decision powered by Interview is a proof point: a domain-specialized model produces better, cheaper, more auditable outputs than a generic frontier model on this task.
The hiring domain is the first. The architecture, training methodology, and evaluation framework are designed to generalize to other high-stakes, evidence-heavy domains.
Explore Debrev InterviewEvidence Input: Interview audio, notes, resumes, scorecards
Strata Processing: Transcription → normalization → rubric-aware model analysis
Structured Output: Candidate insights, competency signals, evidence traceability
Product Integration: Dashboard, AI chat, pool comparison, offer decisions
Cost Profile: Cheap enough to run for every candidate, every round
A methodical, measurable approach to proving a compact vertical model: five phases of architecture, training, and optimization before deployment. Currently executing in the hiring domain.
Establish a specialized model foundation using QLoRA fine-tuning on a base architecture, structured output schemas, and domain-specific datasets. This phase proves core model viability with minimal overhead.
# Fine-tune with QLoRA
model = AutoPeftModelForCausalLM.from_pretrained(base_model, peft_config)
# Structured output schema
output = {competencies: [...], strengths: [...], risks: [...]}
Make the model meaningfully better at domain-specific reasoning. Clean data, remove ambiguous categories, expand edge cases, measure where the model misjudges context.
Success: Tuned model significantly outperforms base on held-out domain examples, with fewer confused labels and better context awareness.
Lock the schema and make outputs production-grade. Add confidence levels, evidence traceability, and explicit reasoning that application logic can consume reliably.
# Structured, machine-consumable output
{
signals: [{ name: "leadership", confidence: 0.87, evidence: [...] }],
risks: [{ type: "communication", severity: "medium" }],
recommendation: "Strong fit", uncertainty: 0.12
}
Let the system use org-specific rubrics, role requirements, and contextual preferences without retraining. One core model, multiple deployment contexts.
Success: Same model adapts to different standards via retrieved context. Org-specific knowledge lives outside model weights.
Make it cheap enough for production. Benchmark latency, throughput, quantization, and local/private runtimes. Viable cost per decision-support action.
Success: Clear cost/quality tradeoff documented. Viable local or server deployment. Cheap enough to run repeatedly for every input.
The guiding constraints that shape how Strata is built and how it behaves.
Decision support, not autonomous decisions. The system informs; humans decide.
JSON schemas, confidence levels, and explicit reasoning — not vague narrative text.
Every claim ties back to source evidence. Full traceability for compliance and accountability.
System state is persistent, versioned, and queryable — not buried in chat history.
Deep specialization in one domain, not generic model usage. Bounded workflows are a feature.
Economically viable to run for many inputs, many times. Production viability is a first-class constraint.
Once the vertical model is proven in the hiring domain, Strata can expand — both deeper within hiring and outward into other evidence-heavy decision domains.
Richer state models that track signals over time, aggregate evidence across multiple inputs, and surface contradictions or information gaps.
Systems that support side-by-side comparison and reviewer alignment — not just individual analysis of single inputs.
Models that identify gaps in evidence and suggest what to gather next — turning the system into an intelligent co-pilot for evidence collection.
Apply the same methodology to other decision-heavy domains: design evaluation, market research, partnership assessment, and beyond.
Debrev Strata is an ongoing initiative into domain-specialized AI. We're advancing each phase of model development, proving the approach in production, and expanding the methodology.