LockedIn Model Foundry
Private models for the workflows generic AI cannot own.
We help enterprises move from broad AI experiments to task-specific models that are cheaper to run, easier to govern, faster to execute, and aligned to the way the business actually works.
Built for regulated systems, legacy data, enterprise controls, and production adoption.
The operating constraint
Most enterprise AI work does not need a bigger model. It needs a better-fit model.
General-purpose models are excellent for broad reasoning, but many enterprise workflows are repetitive, domain-specific, latency-sensitive, cost-sensitive, privacy-sensitive, or compliance-heavy. Running every task through a frontier model can create unnecessary cost, governance complexity, data exposure, and operational risk.
High inference cost
Running every task through a frontier model creates unnecessary per-token spend at enterprise volume.
Latency in operational workflows
Operator-facing and real-time workflows cannot tolerate multi-second round-trips to remote inference APIs.
Sensitive data exposure
Sending confidential, PHI, PII, or regulated data to third-party model APIs introduces compliance and security risk.
Weak task repeatability
General-purpose models produce inconsistent outputs on narrow, repetitive enterprise tasks where precision matters.
Limited auditability
Opaque model behavior makes it difficult to produce the evidence, traceability, and audit trails regulators require.
Model architecture
Model architecture matched to the operating constraint.
LockedIn Model Foundry evaluates the workflow before selecting the model strategy. Some workflows need retrieval. Some need a small language model. Some need a tiny model embedded in an app. Some need an agentic workflow with human review. Some need a hybrid system with deterministic rules, model assistance, and evidence capture.
| Model Type | Best For | Data Requirement | Latency | Governance | Deployment | Example Use Case |
|---|---|---|---|---|---|---|
| Classifier | Binary or multi-class decisions | Labeled examples | Sub-10ms | Low | Edge, API, embedded | Claims triage, ticket routing |
| Extractor | Structured data from unstructured text | Annotated documents | Sub-100ms | Low–Medium | API, batch | KYC extraction, clause parsing |
| Tiny Language Model | High-volume narrow tasks | Task-specific corpus | Sub-50ms | Medium | Edge, on-prem, API | Intent detection, compliance flags |
| Small Language Model | Domain reasoning and generation | Domain corpus + labels | 100–500ms | Medium–High | VPC, cloud, on-prem | Summarization, copilots |
| RAG Workflow | Knowledge-grounded answers | Document corpus + embeddings | 500ms–2s | Medium | Cloud, VPC | Policy Q&A, knowledge base |
| Agentic Workflow | Multi-step orchestration | Tools + context + rules | 2–10s | High | Cloud, managed | Underwriting, audit workflows |
| Frontier LLM | Complex reasoning, open-ended | Prompts + context | 1–5s | High | API | Executive analysis, novel research |
Capability
Private model systems, not AI experiments.
Tiny Language Models
Task-specific models for narrow, high-volume workflows: classification, routing, extraction, intent detection, compliance flags, and call disposition.
Small Language Models
Domain-tuned models for summarization, structured reasoning, workflow guidance, internal copilots, document understanding, and operational decision support.
RAG + SLM Systems
Retrieval-grounded systems combining controlled enterprise knowledge with smaller models for stronger accuracy, explainability, and cost control.
Distillation & Compression
Convert expensive frontier model behavior into smaller, faster systems where the task is stable and measurable.
Evaluation Harnesses
Golden datasets, regression tests, business-rule checks, hallucination checks, latency benchmarks, cost benchmarks, and human review workflows.
Deployment Architecture
Customer cloud, private VPC, on-prem, edge, managed API, secure batch processing, or embedded app deployment.
Build path
From workflow to model in controlled stages.
Executive Model Foundry Session
Executive Model Foundry Session
Understand the business constraint, current workflow, data posture, and operating risk.
Use Case Selection
Use Case Selection
Identify tasks where a smaller model could reduce cost, improve speed, increase control, or automate repeatable work.
Data Readiness
Data Readiness
Review documents, logs, transcripts, tickets, forms, structured data, historical decisions, human labels, and compliance boundaries.
Architecture Decision
Architecture Decision
Choose TLM, SLM, RAG, agentic workflow, classifier, extractor, or hybrid system.
Training or Adaptation
Training or Adaptation
Fine-tune, distill, prompt-tune, embed, retrieve, label, compress, or assemble the model system.
Evaluation
Evaluation
Measure against task accuracy, cost, latency, business rules, safety, auditability, and human acceptance.
Deployment
Deployment
Ship to the customer environment with observability, access controls, audit trails, model cards, and rollback plans.
Managed Improvement
Managed Improvement
Monitor drift, refresh data, improve labels, retrain, re-evaluate, and expand to adjacent workflows.
Enterprise applications
Where private models create immediate enterprise value.
Healthcare
- Claims triage
- Prior authorization intake
- Provider data normalization
- Clinical document summarization
- Member correspondence classification
- Call center note summarization
- Compliance evidence review
Financial Services
- KYC document extraction
- Risk workflow classification
- Advisor support copilots
- Audit evidence summarization
- Policy interpretation assistance
- Customer support routing
Insurance
- Claims intake classification
- Policy document extraction
- Underwriting support
- Renewal risk detection
- Compliance documentation
Enterprise Operations
- Ticket routing
- Internal knowledge copilots
- Procurement intake
- Contract clause extraction
- HR policy assistance
- ERP/CRM workflow summarization
Contact Centers
- Intent classification
- Agent assist
- Call summarization
- QA scoring
- Escalation prediction
- Script adherence checks
Operating model
Private models still need an operating model.
LockedIn Model Foundry is not just model training. It includes the controls required for enterprise adoption: source authority, access boundaries, evals, human checkpoints, model cards, audit logs, deployment approvals, and evidence loops.
Data boundary mapping
Source authority review
Human-in-the-loop approval
Model card generation
Evaluation evidence
Drift monitoring
Access control
Audit export
Rollback path
Compliance documentation
Engagement models
Built for how enterprises buy model work.
Model Foundry Session
Executive working session
A no-cost executive working session to identify the highest-value private model opportunities in your organization.
Deliverables
- Use case map
- Model suitability matrix
- Data readiness view
- Deployment recommendation
- First model sprint scope
Model Feasibility Sprint
1–2 week sprint
A focused sprint to test whether a task-specific model is viable for the target workflow.
Deliverables
- Sample dataset review
- Baseline model strategy
- Evaluation plan
- Cost/latency estimate
- Prototype or architecture sketch
Custom Model Build
4–8 week sprint
Full model design, tuning, evaluation, and deployment into your operating environment.
Deliverables
- Trained or adapted model
- Evaluation harness
- Model card
- Deployment package
- Operator surface
- Governance documentation
Managed Model Operations
Ongoing engagement
Continuous model monitoring, evaluation, retraining, cost optimization, governance updates, and workflow expansion.
Deliverables
- Monthly performance report
- Drift monitoring
- Retraining plan
- Evaluation updates
- Cost optimization
- New workflow roadmap
Enterprise tiers
Scoped to where you are in the model journey.
Assessment
For teams identifying model opportunities.
Build Sprint
For teams ready to create a first private model.
Production Deployment
For teams deploying into cloud, on-prem, or regulated operations.
Managed Operations
For teams that want ongoing monitoring, improvement, and governance.
Vendor-agnostic infrastructure
LockedIn Labs does not sell models for the sake of models. We design the operating layer, the data path, the evaluation method, the deployment surface, and the governance loop around the model. That is the differentiator.
Bring us the workflow.
We'll show you the model path.
Tell us where the work is repetitive, expensive, sensitive, slow, or hard to govern. LockedIn Labs will help determine whether the answer is a tiny model, small model, retrieval system, agentic workflow, or no model at all.