Large language models, engineered to your business — not a generic API call.
Fine-tuning, model evaluation, and private deployment built for you by forward-deployed engineers — so the model performs on your data and your infrastructure, not just a demo prompt.
What our LLM development services cover.
Model selection & evaluation
Benchmarked comparisons across frontier and open models on your real tasks and data — not generic leaderboards.
Fine-tuning & adaptation
Domain and task-specific fine-tuning so the model reflects your terminology, edge cases, and judgment calls.
Private & self-hosted deployment
Models deployed in your cloud or on-prem when data residency, cost, or compliance rules out the public API.
Prompt & context engineering
Structured prompting, context windows, and retrieval tuned until outputs are consistent — not just occasionally good.
Evaluation harnesses & guardrails
Automated test suites that catch regressions before they reach production, plus guardrails for safety and cost.
Cost & latency optimization
Model routing, caching, and quantization so quality holds while inference cost and latency stay predictable.
Built for you, the forward-deployed way.
A senior, AI-augmented engineer embeds with your team, builds on your existing stack, and hands over production-grade software you own.
01
Scoped in 30 minutes
One call with an engineer — not a sales rep — and a fixed-price quote within 48 hours.
02
Working software in week one
AI-native build speed means you see a real, working version fast — and shape it with us daily.
03
Production-grade & owned
Secure, scalable, deployed in your accounts. All code and IP are yours, with no lock-in.
CASE STUDIES
Real interfaces, built on real SaaS
Speed
Customer Success
Speed CSM & Engagement Platform
Internal CS console plus wallet-native engagement games that pay real sats to eligible users
Speed
Fintech
Bank Deposit Guardian
Real-time monitoring, alerting & analytics for bank virtual-account deposits.
The right model and tooling for the job.
Claude
OpenAI
Llama
Mistral
Hugging Face
PyTorch
Your data
+ Your stack
FAQ
Common Questions.
Specific to LLM development.
What does LLM development include?
Model selection and evaluation, fine-tuning on your data, prompt and context engineering, and deployment — private or cloud — with the testing and guardrails to run it in production, not just a demo.
Do you fine-tune or just prompt-engineer?
Can the model run in our own cloud?
Is our data used to train anyone else's model?
How do you know the model is actually reliable?
How is this different from hiring a traditional AI/ML consultancy?