Models that earn,
a place in production.
180+ teams shipping with our engineers · ISO 27001-secure · NDA on request
Zia ZonSource custom AI model demo
Zia · Predictive ML
Online · responds in ~2s
— What we build
Four model types,
one delivery team.
From a from-scratch predictive model to a fine-tuned LLM, we build the model that fits the problem — and the pipeline, evaluation and monitoring around it. No orphaned notebooks.
Custom LLMs & fine-tuning
Domain-tuned language models for copilots, document processing and knowledge systems — grounded in your data, not the open web.
Fine-tuning
LoRA / QLoRA
RAG
Distillation
Predictive & ML models
Forecasting, classification, recommendation, risk and anomaly detection built on your historical and live data.
Forecasting
Churn / risk
Recommenders
Anomaly detection
Computer vision models
Detection, classification, OCR and inspection for images and video — from the factory floor to the clinic.
Detection
OCR
Quality inspection
Imaging
NLP & document intelligence
Extraction, classification, entity and sentiment models that hold up on messy, real-world text.
Extraction
Classification
Sentiment
- Featured · Production ML
Models that ship to production, not to notebooks.
Most custom models die in a Jupyter notebook — great in the demo, gone by week three. We build the whole surrounding system so the model keeps performing after the applause stops.
Data pipelines
Eval harness
MLOps
Drift monitoring
Human-in-the-loop
1
SCOPE
Buy-vs-build call, PoC on your real data
2
BUILD
Train / fine-tune against your KPIs
3
EVALUATE
Offline + online eval gates before launch
Outcome
Every build opens with a proof of concept and a go/no-go. You don’t fund the full model until it’s earned it.
14 YEARS · 300+ PRODUCTS
Built for production,
not for notebooks.
14+
In business
Since 2012
300+
Products
95%
Clients renew
after the first contract
ISO
Audited
Data security
Capabilities & stack
Boring tech that
ships on time.
We pick the smallest model that solves the problem, on the stack you can maintain. Open-source where you need control, closed-source where the ROI is clear. Evaluated, never assumed.
How we pick
01
Buy before build
The most valuable thing we do some weeks is talk you out of a custom model. If a fine-tune or an API wins, we say so.
02
Data before model
Most model failures are data failures. We assess your data — coverage, quality, labels, lineage — before a line of training code.
03
Eval before launch
No model reaches production without passing offline benchmarks and a live shadow test against your KPIs.
04
MLOps by default
Monitoring, drift detection and a retraining path are in scope from day one — not a phase-two afterthought.
LANGUAGE & FOUNDATION MODELS
GPT-4.1
Claude Sonnet 4.5
Claude Opus 4
Gemini 2.5 Pro
Llama 3.1 / 3.3
Mistral Large
DeepSeek-V3
Qwen 2.5
Phi-4
ML & DEEP LEARNING FRAMEWORKS
PyTorch
TensorFlow
scikit-learn
XGBoost
LightGBM
Hugging Face
Keras
FINE-TUNING & OPTIMIZATION
LoRA / QLoRA
PEFT
RLHF / DPO
Quantization
Distillation
Hyperparameter tuning
DATA & VECTOR
Snowflake
BigQuery
Airflow
Ragas
TruLens
OpenLLMetry
PromptLayer
DEPLOYMENT
AWS
Azure
GCP
On-prem
Edge / on-device
Docker
Kubernetes
Industries
Models, tuned for
your industry.
Every domain has its own data, edge cases and rules. We bring playbooks, not blank slates — and adapt them to your data from day one.
Healthcare
Finance & Fintech
Retail & E-commerce
Engagement models
Pick how you want
to ship with us.
Three ways in — all senior teams, all fixed against outcomes we agree before the first line of code.
Fixed-scope
Project-based build
Best when you’re not yet sure a custom model is the answer.
- Fixed price, 4–8 weeks
- PoC on your real data
- Performance baseline + a straight go/no-go
- No commitment beyond the PoC
Most popular
Fixed-scope
Project-based build
- Discovery → deploy in 8–14 weeks
- Fixed scope, fixed price — no hourly drift
- Eval harness + monitoring included
- Handover docs + reference architecture
Embedded
Staff augmentation
Plug vetted senior ML engineers into your team
- 8+ yr average, hand-picked, never on a bench
- Time-zone aligned, full-stack or specialist
- Direct line to your ML or data lead
FAQ
The questions everyone asks before kickoff.
Still deciding? Book a 30-min call with a senior ML engineer.
Should we build a custom model, or just use an off-the-shelf API?
Often the API wins — and we’ll say so on the first call. A custom model earns its cost when your data is proprietary, your domain is specialized, your compliance rules are strict, or accuracy from a generic model plateaus below what the business needs. We help you decide before you spend.
How long does a custom AI model take, end to end?
A focused PoC on one use case runs about 4–8 weeks. A production-grade model — data pipelines, training, evaluation, integration and deployment — typically lands in 8–14 weeks, depending on data readiness and integration surface.
What does custom AI model development cost?
It’s priced by scope, not by the hour — you get a fixed number before we start. A PoC is a small, capped engagement; a production build is quoted against the specific model, data work and integrations. Book a call and we’ll come back with a fixed-scope figure.
How much data do we need, and what kind?
Less than most teams fear for a fine-tune or a well-scoped predictive model; more than you’d hope if the data is unlabeled or scattered. We start with a data assessment — coverage, quality, labels, lineage — and tell you exactly what’s usable and what’s missing before training.
Who owns the model, the weights and the IP?
You do. You keep the trained model, the weights and the reference architecture. No lock-in, no rented intelligence.
How do you handle security, PII and compliance?
We’re ISO 9001:2015 and ISO 27001:2018 certified, audited annually. We’re NDA-friendly and DPA-ready, and we can train and deploy on-prem or in your cloud so sensitive data never leaves your boundary.
How do you keep the model accurate after launch?
Monitoring and drift detection ship with the model, with a retraining path agreed up front. When live data moves away from training data, you’ll know before your users do.
Can the model run on-prem or on-device?
Yes. When latency, bandwidth or data-sovereignty rules out the cloud, we optimize and quantize the model for on-prem or edge deployment.
ISO 27001-secure · Certified since 2020
Let's build a model worth shipping.
Send your brief — a senior ML engineer (not a sales rep) replies within 24 hours with a buy-vs-build read and a fixed-scope plan.
- A 30-min scoping call with a senior ML engineer
- A buy-vs-build recommendation for your use case
- A fixed-scope PoC plan with a go/no-go gate
- A reference architecture diagram — yours to keep
- Reply within 24 hours · NDA-friendly · Senior team only
