When an API is the wrong default
Cost, latency, or data-residency can make a generic endpoint the expensive option. We quantify that before we train.
Custom LLM development
Domain models you can host and operate
Fine-tune, distill, or privately host a model when a generic API is too costly, too generic, or cannot leave your network. You own the weights we train for you.
- Fine-tuning
- Private hosting
- Your weights
Decision matrix
When a custom LLM is the right spend
Most products should start with a hosted API plus RAG. These are the cases where training or private serving earns the cost.
SignalWhat it usually means
Data cannot leave the network
Healthcare, finance, or defense constraints that rule out sending prompts to a public API.
Domain language
Legal, industrial, or internal jargon a general model keeps getting wrong even with retrieval.
Unit economics
High volume where a distilled local model beats per-token pricing within months.
Latency on-device or on-prem
Inference next to the factory, clinic, or store — not a round trip to a US region.
PrivateVPC or on-prem serving
EvalsBefore any production cutover
6–14 wksTypical model + serving slice
100%Trained weights you own
What you get
Outcomes that survive the training run
Fine-tuning is only half the job. Serving, evals, and product hooks ship with the weights.
Fine-tune on your examples
Instruction data from your tickets, reports, or style guide — not a scraped internet dump.
Serve where the data already lives
vLLM, TGI, or managed endpoints in your AWS, GCP, Azure, or on-prem GPU cluster.
Weights in your account
Adapters and checkpoints we train for you transfer. Base-model licenses stay what they are.
Lab work
What custom LLM work includes
We only train when a smaller prompt, RAG, or a cheaper API cannot hit the bar. When we do train, serving and evals ship with the model — not a notebook.
01
Decision
Build vs API vs RAG
We measure the generic model on your tasks first. Fine-tuning starts only if retrieval or prompting cannot meet quality, cost, or residency.
- Baseline on your eval set
- Cost model for tokens vs GPUs
- Written go / no-go before training
02
Data
Instruction and preference sets
We turn tickets, reports, and expert edits into training rows — with PII handling and a hold-out set the training run never sees.
- PII redaction and review
- Hold-out evaluation data
- Style and format constraints
03
Training
Fine-tune and distill
LoRA / QLoRA adapters, continued pre-training on domain text, or distillation into a smaller model you can actually afford to serve.
- Adapter training on your GPUs or ours
- Distillation for latency budgets
- Checkpointing you can roll back
04
Serving
Private inference
Open-weight or licensed models behind your auth, with batching, quantization, and autoscaling matched to traffic — not a demo GPU left on overnight.
- vLLM / TGI / managed endpoints
- GPU rightsizing
- Auth, rate limits, and tracing
05
Quality
Offline and online evals
Task scores before cutover, shadow traffic against the API you already use, and regression gates so a new adapter cannot tank support overnight.
- Task-specific metrics
- Shadow vs production API
- Rollback to the previous adapter
06
Product
The application around the model
A custom LLM is useless as a checkpoint. We ship the API, admin, and client that call it — web, mobile, or an internal tool.
- OpenAI-compatible internal API
- Streaming in the product UI
- Prompt and version management
How we work
From go / no-go to a model you can operate
We prove the hosted API first. Training starts only when the numbers and constraints say it should.
- 01
Discuss requirements
A short call on the decision you need, existing systems, data, and whether custom AI is the right spend. Mutual NDA first.
- Requirements review
- Technical approach
- 02
Define the solution
Architecture, scope, timeline, and cost in a written proposal — including evals, cloud, and what you will own at handover.
- Architecture blueprint
- Scope and timeline
- 03
Develop the solution
Engineers build and test with regular working releases on shared staging. You see retrieval quality, vision accuracy, or agent tool use on real examples.
- Weekly working releases
- Testing and verification
- 04
Deploy and handover
We deploy into your cloud, transfer repositories, documentation, and IP, and leave monitoring so the system does not silently drift.
- Your cloud
- 100% source and IP
Why train with Zupitrix
Tangible reasons to choose Zupitrix
Senior engineers only
The people who scope the project build it. You are not handed off from a sales deck to a rotating bench.
Honest feasibility
If a SaaS tool or a rules engine is enough, we say so on the first call. Custom AI has to earn the spend.
Your code and IP
Repositories, prompts, eval sets, and models we train for you sit in your accounts. No vendor lock-in tax.
Direct communication
Backlog, pull requests, and weekly demos. You always know what shipped, what is blocked, and why.
Case studies
Related work we have shipped
LLM · Language learning
Fluent Fox
Conversational practice with virtual AI tutors — short mobile sessions, not a desktop chatbot wrapped in a WebView.
- LLM
- Tutor conversations
- Mobile
- Practice loops
- Live
- Learning product
Education · Retrieval + matching
AI Learning Platform
Students find experts, book live sessions, and learn on their schedule — Next.js, Python, and AWS behind the marketplace.
- RAG
- Knowledge + matching
- AWS
- Production cloud
- Web
- Full-stack app
FAQ
Questions about custom llm development
Need custom llm development?
Tell us why a generic API is not enough
We will score the hosted model on your task, estimate GPU cost, and say whether training is justified before anyone spins up a job.
Keep exploring
Related AI services
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