Move Beyond Shared AI Toward Owned Enterprise Intelligence
The frontier-model race has accelerated adoption The same API is one call away for every competitor — but differentiation now comes from models trained on your proprietary enterprise data, domain expertise and operating knowledge. The question is no longer whether you use AI. It is whether you own the intelligence powering it.
Context Makes AI Informed. Owned Models Make It Expert
Enterprises are investing heavily in context — retrieval pipelines, larger context windows, richer prompts. It works, and better context always produces better outcomes. Context is most powerful when it lives inside the model, not beside it. Embedded into a model you own, your knowledge becomes part of how the model reasons, reflecting your vocabulary, decision patterns, and business priorities. The model retains what matters, delivers more precise and domain-specific outputs, and reduces the need to repeatedly provide the same information with every interaction. Context stops being a temporary prompt and becomes a durable capability.
Context and owned models are one strategy, not two. Better context produces better training signal; a domain-tuned model needs less context stuffed into every prompt to reach the same answer. Each makes the other cheaper and sharper.
That is where the Model Building Studio creates value. It combines expert-led delivery with a purpose-built, no-code platform to help enterprises move from API consumer to model owner — turning proprietary data into a defensible AI asset, governed by your rules, deployed privately by default, and compounding in value with every interaction and cost-quality optimisation.
What We Deliver
Model Building Studio is two things working together — a practitioner-led service and a no-code platform. Engage with either, or both.
Model Building as a Service
Our practitioners run the build with you: discovery and blueprint, data engineering, training and fine-tuning, evaluation and ModelOps. You get a working domain model and the team that knows how it was made.
Model Building Studio Platform
A no-code platform covering the full model value chain: prepare data, train on any open base model, benchmark against your own success criteria, deploy with versioning and rollback. Reusable workflows mean the second model costs a fraction of the first.
How Model Building Transforms Real Enterprise Workflows
Model Building turns dependence on shared, per-call frontier LLMs into owned, domain-specialised intelligence — making accuracy, cost and control explicit across regulated, high-volume operations.
Banking & Financial Services – Risk, Underwriting & Sovereign AI
From: Reliance on frontier-model APIs for credit, chat and summarisation, with rising per-call costs, vendor lock-in and data-localisation exposure
To: Owned domain SLMs — CPT on credit policies, filings and transaction history — deployed on-prem for sovereign, auditable inference at 10–40x lower cost at scale
Healthcare & Life Sciences – Drug Discovery & GxP Document Extraction
From: Multi-modal biomedical data locked in silos and handwritten GxP records that defeat OCR, while compliance bars any public LLM from touching the data
To: Privately fine-tuned domain and vision-language models delivering 60% relevance gains, 50% faster hypotheses and ~90% extraction on handwritten records — fully private and GxP-safe
Technology & Telecom – Product-Embedded & Engineering Knowledge Models
From: Per-call costs eroding product margins, latency budgets frontier APIs cannot meet, and no differentiation on shared model APIs
To: Owned SLMs behind high-volume product flows and CPT on specs, tickets and telemetry — plus edge fine-tuning for on-device AI without connectivity dependence
Retail, CPG & Consumer – Brand-Safe & Demand Intelligence Models
From: Off-brand AI output eroding customer trust, copyright exposure from generic content, and API costs on high-volume shopper interactions
To: Preference-aligned brand-voice models and CPT on transactions and supply data, with edge models powering offline-capable, low-latency in-store experiences
From Data to a Deployed, Owned Model
Phases in Model Building — Model Ownership Is Not a Project. It Is a Journey.
Owning a model isn’t a one-off build that ends at launch. One platform and one service model cover the whole journey, in five phases — each ending with something tangible you can sign off on.
Discovery & Intent Alignment
Business problem is defined, the model must solve and agree what success looks like before any building starts.
Output: A build blueprint and a data readiness view.
Data Engineering & Readiness
Data is profiled, cleaned, organised, labelled and enriched until it’s fit to teach a model; where real data is thin, synthetic examples fill the gap.
Output: A certified dataset with regulator-ready lineage.
Model Selection & Building
An open, proven base model is trained on the Client’s domain so vocabulary and reasoning become the part of the model itself.
Output: A trained model and benchmark report.
Production-Grade Evaluation
Model is tested against benchmarks designed and setup by the organisation and not generic leaderboards.
Output: A readiness certificate and a model card — what it does, how well, and where its limits are.
ModelOps & Continuous Learning
The model is watched for drift, retrained when the world shifts, and improved through feedback driven periodic training.
Output: A live dashboard and a running retraining pipeline.
How Model Building Drives Value Owning the model changes the economics, the accuracy and the control of every AI-heavy operation.
Build a Data Moat
Turn proprietary enterprise knowledge into a unique competitive asset that compounds with every interaction.
Improve Domain Accuracy
Outperform general-purpose models on enterprise-specific tasks, with 20–40% precision gains on domain workloads.
Reduce Inference Costs at Scale
Replace per-call frontier APIs with owned SLMs for up to 10–40x lower inference cost at production volume.
Ensure AI Sovereignty & Compliance
Keep data, models and inference under enterprise control with on-prem, auditable deployments and transparent, regulator-ready lineage.
Compound Intelligence & Own Your IP
Retain 100% of the weights, benchmark and runbook while every cycle strengthens enterprise-specific performance.
Achieve Vendor Independence
Move from API consumer to model owner, removing lock-in and protecting sensitive intellectual property.
Proven in Real-World Environments
From drug discovery to document intelligence, owned models built on the Studio deliver frontier-grade performance at enterprise scale.
20–40%
Precision gains
Domain tasks vs. general frontier models
10–30x
Cost reduction
Owned SLMs vs. per-call frontier APIs
100%
Model ownership
Weights, benchmark and runbook are yours
Selected outcomes
- Drug Discovery: 60% improvement in relevance and 50% faster hypothesis generation on proprietary clinical and biomedical data.
- Intelligent Document Processing: ~90% extraction accuracy on complex handwritten records — fully private.
- Synthetic Data Generation: Training-grade data with zero PII exposure via a compliant, on-prem pipeline.
- Credit Risk (BFSI): Owned on-prem underwriting model correct on 19 of 20 cases on the bank’s own benchmark, against 16 of 20 for the frontier-model baseline — every decision with a cited rationale.
- Clinical Operations (HLS): Compact domain model pre-trained on CRO corpora for trial management, patient recruitment and regulatory submissions — private by design.
Why Persistent?
End-to-end model-building expertise
Persistent brings together data engineering, model selection, fine-tuning, evaluation and ModelOps to take enterprise AI from initial blueprint to production-ready deployment.
Ownership, privacy and control by design
Build on proprietary enterprise data, deploy in private environments and retain full ownership of model weights, benchmarks and runbooks, reducing lock-in and supporting regulatory requirements.
Proven domain outcomes at enterprise scale
Combine deep industry knowledge with reusable, no-code workflows to improve domain accuracy, lower inference costs and accelerate the path from the first model to a repeatable model-building capability.
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FAQs
AI model building is the process of creating, training, fine-tuning, evaluating and deploying an AI model for a specific business use case. For enterprises, it often involves using proprietary data to build private, domain-specific models that improve accuracy, control and cost efficiency.
General-purpose AI models are trained for broad use across many industries. AI model building helps enterprises create or fine-tune models on their own data, domain language and workflows, making the model more relevant, accurate and aligned to business-specific tasks.
Enterprises build their own AI models to improve domain accuracy, reduce dependency on shared APIs, protect sensitive data and gain ownership over model weights, benchmarks and runbooks. Owned models also help reduce inference costs at scale and support stronger governance.
AI model training typically requires relevant, high-quality enterprise data such as documents, policies, transactions, customer interactions, product data, operational records or domain-specific knowledge. The data must be cleaned, labelled, structured and validated before training or fine-tuning begins.
AI model fine-tuning is the process of adapting a pre-trained model to perform better on a specific domain, task or business context. It helps the model understand enterprise terminology, decision patterns and industry-specific requirements more accurately.
LLM fine-tuning is the process of training a large language model on specialised data so it can deliver more accurate, relevant and controlled responses for specific enterprise tasks. It is useful when prompts or retrieval alone are not enough to achieve the required quality, consistency or domain depth.
ModelOps is the operational discipline of managing AI models after deployment. It includes version control, monitoring, drift detection, retraining, performance evaluation and governance to ensure the model remains accurate, compliant and production-ready over time.
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