“aiAble improved policy-safe output quality by 29% while cutting token spend by 38%. We scaled moderation and creative generation without adding headcount.”
Head of ML Operations, Global Retail Ads Platform
Fine-Tuned Open-Source LLMs for Ads
Custom models for ads teams, trained on your organizational data.
aiAble adapts open-source LLMs to your internal policies, historical ads, brand guidelines, taxonomies, and workflows. Run private, high-throughput workloads with lower latency, tighter token efficiency, and enterprise-grade reliability.
- Private data adaptation
- Low-latency inference
- Token-efficient generation
- Enterprise-grade deployment
Inference Console
LiveInput Context
Brand policy v4 • Marketplace taxonomy • Arabic locale constraints • Seasonal campaign rules
P95 Latency
118ms
Token Savings
-42%
Quality Score (Internal Eval)
Fine-tuned model outperforms baseline prompting on policy adherence and tone consistency.
Trusted by Ads and Commerce Teams
NOVA MEDIA
PIXEL CART
ADLIFT CO
MARKETSPARK
BRANDGRID
OMNISTORE
“By training on our internal taxonomy and workflow history, we reduced post-editing effort and shipped multilingual campaigns faster at lower cost per request.”
Director of Ad Intelligence, Enterprise Marketplace
What we do
A model lifecycle designed for enterprise ad systems where consistency, speed, and governance are non-negotiable.
Domain & organization-specific fine-tuning
We tune open-source models on your policies, approved ad archives, brand rules, and internal semantics for outputs that match your business logic.
Custom inference and serving stack
Our serving layer is optimized for ads traffic patterns, with aggressive caching, batching, and routing for predictable low-latency throughput.
Continuous evaluation & drift monitoring
Track policy adherence, category accuracy, and brand alignment over time with automated regression checks and drift alerts.
Built for ads teams
Pre-optimized for critical ad operations that rely on private rules, nuanced taxonomy logic, and channel-specific output standards.
Moderation & policy compliance
Enforce internal rulesets for safe and platform-compliant outputs.
Category & tagging
Classify content using your internal taxonomy and marketplace schema.
Creative variants
Generate high-quality variants aligned with campaign goals and brand voice.
Brand tone alignment
Maintain tone consistency across channels, regions, and product lines.
Multilingual (including Arabic)
Support nuanced multilingual generation with locale-aware constraints.
Marketplace-specific optimization
Tune output style and constraints to each retail media and marketplace environment.
Performance
Custom training on organizational data reduces prompt overhead, improves cache locality, and cuts end-to-end inference cost at scale.
| Metric | Generic Prompting | aiAble Fine-Tuned |
|---|---|---|
| P95 Inference Latency | 285ms | 118ms |
| Average Prompt Tokens | 1,840 | 1,060 |
| Monthly Token Usage | 1.00x baseline | 0.58x |
| Cache Hit Rate | 41% | 69% |
Faster inference on private data
Reduced prompt size
Lower token usage
Better cache utilization
Security & privacy
Architecture designed for enterprise confidentiality and strict workload isolation.
- Full isolation of customer datasets
- Optional on-prem or VPC deployment
- Encryption at rest and in transit
- No cross-customer training
- No training on customer data by default
Get a Quote
Share your workload profile and we will provide a tailored architecture and pricing proposal.
We respond within 24 hours
FAQ
How is our organizational data used?
Your data is used to fine-tune and evaluate models for your own workloads only, enabling outputs that match your policy, taxonomy, and brand requirements.
What does onboarding look like?
We map your datasets, validate quality, run a pilot fine-tune, then benchmark against your baseline before production rollout with monitoring enabled.
Fine-tuning vs RAG vs prompting?
RAG and prompting remain useful, but fine-tuning on stable internal patterns reduces prompt bloat, improves consistency, and lowers per-request cost at scale.
Do you provide latency guarantees?
Yes. SLA targets are defined by deployment shape and throughput tier, with transparent P95/P99 reporting and alerting.
How do you optimize cost?
We reduce prompt overhead, optimize serving paths, and tune model size-to-quality ratios to reduce token and infrastructure spend.
What deployment options are available?
Managed cloud, private VPC, and on-prem options are available depending on compliance, latency, and data residency requirements.
What support model is included?
Enterprise plans include dedicated technical support, regular model reviews, and ongoing optimization guidance aligned to your roadmap.