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LLM Development Services
Purpose-built AI models when off-the-shelf APIs can't meet your accuracy, latency, or data privacy requirements.
Service
Custom Model Development
Best for
Differentiated, IP-critical problems where a public API can't meet accuracy, speed, or data-residency needs.
Signature outcome
An adaptive pricing model trained across 6 regional markets, delivering a 4.2% margin lift.
Overview
Most AI problems are solved by calling an existing model. Some are not. When accuracy on your specific task plateaus, when latency budgets are measured in milliseconds, or when your data cannot leave a particular jurisdiction, you need a model built around your problem instead of a general one rented by the call.
Our LLM and custom model development services cover that path end to end: framing the problem so it is measurable, preparing and labelling data with clear lineage, choosing between fine-tuning an open-weight model and training a purpose-built one, and then the part most projects underestimate, getting it into production with versioning, monitoring, rollback and scheduled retraining.
This is for differentiated, IP-critical problems where the model itself is part of your competitive position: pricing, risk scoring, domain-specific extraction, forecasting, or on-device intelligence with hard constraints. We are equally willing to run the benchmark and tell you a hosted API already wins, which is a cheaper answer than finding out after six months of training runs.
How we approach it
Some problems define your competitive edge and need a model no vendor can sell you. We design a model around your specific data, train it on your infrastructure or ours, and deploy it with the reliability and scaling your production systems need. Everything is tracked, versioned, and monitored so accuracy stays high and you always know what version is live.
Business questions we answer
“What differentiated problem in your business deserves a model built specifically for you?”
What you get
- Custom model designed around your specific problem
- Training on your data with full data lineage
- Production deployment with scaling and monitoring
- Safe rollout strategy with fallback protection
- Ongoing accuracy monitoring and scheduled retraining
Frequently asked
LLM Development Services: common questions
What are LLM development services?
They cover building and adapting language models for a specific task: data preparation, fine-tuning or training, evaluation against a task-specific benchmark, and production deployment with monitoring and retraining. It is distinct from prompt work on a hosted API, which we do first when it is sufficient.
When should we fine-tune instead of using a hosted model?
Fine-tune when a strong prompt plus retrieval still misses your accuracy target, when you need consistent output structure at high volume, when per-call pricing at your volume exceeds hosting your own, or when data residency rules out sending content to a third party.
How much data do we need to fine-tune a model?
For instruction-style fine-tuning, a few thousand high-quality labelled examples usually beats hundreds of thousands of noisy ones. We audit what you have first and often generate a labelled set from historical records rather than starting from scratch.
Can the model run in our own cloud or on-premise?
Yes. Open-weight models can be deployed in your cloud tenancy, in a specific region, on-premise, or at the edge for latency-critical or disconnected use. We size the hardware and give you the cost per thousand requests up front.
Who owns the model and the training data?
You do. Weights, training pipelines, evaluation sets and documentation are delivered into your repositories and infrastructure, with no dependency on us to keep running.
What happens after the model goes live?
We monitor accuracy and drift against live traffic, keep a versioned rollback path, and set a retraining cadence tied to how fast your data changes. Your team can take that over immediately or we can run it under a support agreement.
Proof in production
Relevant case studies
Let’s solve your data challenge