AI Development
Production AI applications, agentic systems, LLM fine-tuning, and data pipelines — built by the team that published research on AI agent design and trained LLMs from scratch. Deterministic where possible, observable everywhere, and engineered to produce reliable outputs instead of expensive hallucinations.
We have trained LLMs from scratch, built open-source NLP libraries used across the legal industry (LexNLP, 102+ citations), created benchmark datasets adopted by researchers worldwide (LexGLUE, 422+ citations), and shipped production AI systems for Fortune 500 companies. We build AI that works reliably because we understand the engineering, not just the API calls.
Starting at $30K | 4-24 weeks
Services
AI Application Development
Production AI applications from proof-of-concept through deployment. Structured outputs, evaluation harnesses, fallback logic, and observability built in from the start.
4-16 weeks
AI Agent Design & Implementation
Agentic AI systems with defined tool boundaries, retry logic, human-in-the-loop checkpoints, and deterministic control flows. Architecture grounded in our published agent design research.
4-12 weeks
LLM Fine-Tuning & Custom Models
Domain-specific models trained on your data with rigorous evaluation: held-out test sets, regression benchmarks, and production monitoring. Copyright-clean data methodology from our KL3M experience.
4-12 weeks
Data Pipeline & Infrastructure
Data ingestion, processing, and serving infrastructure for AI workloads. Schema validation, data quality checks, lineage tracking, and ML training pipelines with reproducible builds.
4-12 weeks
Evaluation & Observability
Systematic evaluation frameworks for AI systems: automated test suites, drift detection, output quality monitoring, latency tracking, and cost attribution. Know when your system degrades before your users do.
2-6 weeks
Legacy System Modernization
AI-accelerated assessment and migration of legacy systems. Code analysis, architecture redesign, and incremental migration with minimal disruption.
8-24 weeks
Why us
We've trained LLMs from scratch
KL3M was not fine-tuning on top of someone else's model. It was training from scratch: 132M+ documents, custom tokenizers, domain-specific architecture, and rigorous evaluation against held-out benchmarks. When we build AI for you, the engineering decisions come from that depth of experience, not from a weekend tutorial.
Engineering for reliability, not demos
Structured outputs instead of raw completions. Evaluation harnesses that catch regressions before deployment. Fallback logic for when the model is uncertain. Observability that tells you exactly what the system is doing, why, and how much it costs. Production AI needs engineering discipline, not prompt magic.
Published research on agent architecture
Our 2025 paper "How to Design an AI Agent" covers architectures, protocols, and evaluation frameworks for agentic systems. We build agents with defined tool boundaries, retry logic, human-in-the-loop checkpoints, and deterministic control flows — not open-ended prompt chains that work until they don't.
Why licens.io?
| Big 4 | licens.io | |
|---|---|---|
| AI depth | Wrapper around vendor APIs | Trained LLMs from scratch, published tokenizers and datasets |
| Reliability | Demo works, production breaks | Evaluation harnesses, drift detection, fallback logic |
| Agent design | Ad hoc prompt chains | Published agent architecture research with defined tool boundaries |
| Observability | Logs and hope | Structured tracing, cost attribution, output quality monitoring |
| Track record | Building AI practices | LexNLP, LexGLUE, KL3M: 4,000+ citations, Fortune 500 clients |
| Pricing | Hourly, $150-250/hr | Fixed-fee, $30K-$300K |
AI depth
Big 4
Wrapper around vendor APIs
licens.io
Trained LLMs from scratch, published tokenizers and datasets
Reliability
Big 4
Demo works, production breaks
licens.io
Evaluation harnesses, drift detection, fallback logic
Agent design
Big 4
Ad hoc prompt chains
licens.io
Published agent architecture research with defined tool boundaries
Observability
Big 4
Logs and hope
licens.io
Structured tracing, cost attribution, output quality monitoring
Track record
Big 4
Building AI practices
licens.io
LexNLP, LexGLUE, KL3M: 4,000+ citations, Fortune 500 clients
Pricing
Big 4
Hourly, $150-250/hr
licens.io
Fixed-fee, $30K-$300K
Who this is for
- ✓ Enterprises needing production AI with compliance requirements met from day one, not retrofitted later
- ✓ PE/VC portfolio companies building AI capabilities across their holdings
- ✓ Regulated industries (finance, healthcare, legal) where AI systems must satisfy compliance requirements from architecture through deployment
- ✓ Companies needing agentic AI systems designed with governance and accountability built in
- ✓ Organizations modernizing legacy systems that need AI-accelerated migration without disruption
Frequently asked questions
How is your AI development different from a typical dev shop?
Most shops build a demo that works on the happy path and hand it off. We build systems with evaluation harnesses, structured outputs, fallback logic, and observability from day one. We have trained LLMs from scratch, published benchmark datasets, and shipped production AI for Fortune 500 companies. The difference shows up in production, not in the pitch deck.
How much does custom AI development cost?
AI application development typically runs $50K-$250K depending on scope. LLM fine-tuning is $40K-$150K. Data pipeline builds are $30K-$100K. All quoted as fixed-fee engagements with defined deliverables.
Should I build or buy an AI agent?
It depends on how central the agent is to your business, how sensitive the data is, and what regulatory requirements apply. If your use case involves regulated processes, proprietary data, or differentiated workflows, building gives you more control. We help you make that assessment grounded in our published agent design research.
Can you fine-tune on our proprietary data?
Yes. We fine-tune and train models on client data with strict data handling protocols. Our experience with KL3M's copyright-clean data pipeline means we understand data provenance, licensing, and governance throughout the training process.
How do you handle evaluation and testing for AI systems?
Every system ships with an evaluation framework: held-out test sets, regression benchmarks, automated quality checks on outputs, and drift detection in production. We define what "good" looks like before writing the first line of code, then measure against it continuously. If the system degrades, you know before your users do.
How long does a typical engagement take?
Data pipeline builds run 4-12 weeks. AI application development takes 4-16 weeks. Legacy modernization runs 8-24 weeks. Each engagement is scoped with clear milestones and deliverables upfront.
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