Custom-engineered AI. Built exclusively for your business goals.
General-purpose AI is built for the average use case which means it is not optimized for one particular use. At IBaseIT, we build AI models that are trained on your data, evaluated against your standards, and fine-tuned to perform on your specific tasks. From innovation to production-grade scale we build custom AI solutions through our propreitary IBaseONE Framework.
A model is only as good
as the data it was
trained on. And tested against.
At IBaseIT,we start with innovation identifying the right architecture, the right data, the right fine-tuning approach for your specific use case. Then we scale it: hardening the model for production, embedding it in your workflows, and monitoring its performance continuously through our Agile AI methodology. From 0 to 1 and from 1 to many that is how we deliver AI on the Move through the IBaseONE Framework. Simplicity that Solves. AI that Empowers.
General AI is built for everyone. Yours should be built for you.
A model fine-tuned on your data outperforms a general model on your tasks. Every time.
Foundation and popular commercial LLMs are extraordinarily capable general-purpose systems. They are also optimized for the statistical average of all training data which means they underperform on your specific domain tasks, misunderstand your vocabulary, and produce outputs that need significant post-processing to meet your quality and compliance standards.
At IBaseIT, we fine-tune foundation models on your enterprise data to produce domain-specific models that outperform general alternatives on your tasks at lower inference cost, with higher accuracy, and with outputs aligned to your regulatory and quality requirements from the first token. Our Agile AI sprint model means fine-tuned models reach production in weeks, not quarters and improve continuously as new domain data accumulates.
Domain vocabulary and terminology
When your use case requires the model to understand industry-specific language, proprietary terminology, or regulatory vocabulary that general-purpose training data underrepresents insurance, clinical, legal, or financial domains are typical examples.
Compliance-aligned output formatting
When model outputs must conform to specific regulatory language, tone standards, or structural requirements that cannot be reliably achieved through prompt engineering alone particularly in regulated industries where every output is potentially auditable.
Inference cost at production scale
When the volume of model calls in production makes running large general models uneconomical smaller fine-tuned models often outperform larger general models on specific domain tasks at significantly lower per-call inference cost.
Proprietary business context
When your AI system needs to understand your internal processes, product catalogue, client history, or organizational knowledge that no public dataset contains fine-tuning on your data is the only way to embed this context reliably.
Supervised Fine-Tuning (SFT)
Train on labelled input-output pairs from your domain. Best for task-specific performance improvement where you have quality labelled examples available.
RLHF Preference Alignment
Align model behaviour to human preference rankings. Best for output quality alignment tone, compliance language, brand voice where the right answer is a matter of judgement.
LoRA / QLoRA Efficient Adaptation
Fine-tune specific model layers at a fraction of full retraining cost. Best for cost-constrained environments or where speed of iteration is prioritised over absolute performance ceiling.
Continuous Retraining Pipeline
Automated retraining as new domain data accumulates. Best for production models that must stay current the innovation-to-scaling engine that keeps AI on the Move improving.
Credit & Risk Intelligence
AI that understands financial products, KYC requirements, AML obligations, and credit assessment logic producing faster, more accurate, and more explainable decisions than traditional scorecard models, at scale.
Claims & Underwriting Intelligence
AI trained on claims history, underwriting guidelines, and policy language accelerating claims assessment, supporting underwriting decisions, and detecting fraud patterns with domain accuracy that general models cannot match.
Clinical Decision Support
AI that understands clinical terminology, FHIR standards, ICD codes, and diagnostic reasoning supporting clinicians with evidence-based recommendations without replacing clinical judgment.
Code Generation & Engineering Intelligence
AI fine-tuned on your codebase, your architectural patterns, and your engineering standards accelerating development, reducing defects, and enforcing consistency across teams at scale.
AI that actually understands your industry.
The difference between domain AI and generic AI is not just accuracy it is trustworthiness.
A general-purpose AI model can describe what a credit risk score is. A domain-specific credit risk model can calculate one for your specific products, your specific customer segments, and your specific regulatory environment and explain its reasoning in the language your risk committee uses.
That difference is what 14+ years of enterprise delivery across banking, insurance, healthcare, and hi-tech gives us. We bring genuine domain knowledge to AI development not just the machine learning expertise to fine-tune a model, but the industry depth to know what the model should produce, what it should never produce, and how to evaluate whether it is performing correctly. From innovation prototype to production-scale deployment: domain AI built to last.
Domain knowledge embedded in training
At IBaseIT, we combine machine learning engineering with genuine industry expertise bringing IBaseIT's 14+ years of sector delivery into the data selection, annotation guidelines, and evaluation criteria that shape what your domain model learns and how it behaves.
Regulatory and compliance alignment by design
Domain AI for regulated industries is designed around the compliance requirements of that sector from the architecture stage not reviewed against regulation at the end of development. GDPR, FCA, HIPAA, and sector-specific AI regulation built in.
Agile AI iteration from innovation to scale
We start with an innovation sprint a focused proof of value using your real domain data. Once the model demonstrates domain accuracy, we move to the scaling engine: production hardening, MLOps integration, and continuous improvement pipelines that keep performance compounding.
The world is multimodal. Your AI should be too.
Single-modal AI processes one type of data. Enterprise data is never just one type.
Different industries handle different types of data. An insurance claim contains images and text. A customer service interaction combines voice audio and account records. A manufacturing quality inspection merges camera feeds with sensor readings. Single-modal AI processes one and ignores the rest missing the context that lives in the combination.
At IBaseIT,we build Multimodal AI systems that perceive and reason across text, image, audio, video, and structured data simultaneously producing intelligence that no single-modal model can match. From the innovation stage where we prove cross-modal value on your specific data, to full-scale production deployment across cloud, on-premises, or edge environments via the IBaseONE Framework.
Vision + Language systems
AI that reads images and understands their meaning in natural language context insurance damage assessment from photographs, medical scan analysis combined with clinical notes, document intelligence combining OCR output with semantic understanding.
Audio + Text intelligence
AI that listens and reads simultaneously transcribing speech, understanding tone and sentiment, and combining spoken language with written context to produce complete, actionable intelligence from every conversation and interaction.
Document intelligence and IDP
Layout-aware document models that extract, classify, validate, and action information from complex unstructured documents combining OCR, layout understanding, and language reasoning into end-to-end intelligent document processing pipelines.
Cross-modal fusion architectures
The most sophisticated tier where understanding one modality actively enriches interpretation of another. At IBaseIT,we build genuine fusion architectures rather than connecting single-modal models with brittle pipelines, producing intelligence that compounds across every modality combination.
Vision + Language
Images understood in natural language context. Documents, scans, photographs with reasoning.
Audio + Text
Speech understood alongside written context. Call intelligence that reads between the lines.
Document IDP
Unstructured documents extracted, classified, and actioned. OCR meets semantic reasoning.
Video Understanding
Event detection, object tracking, and temporal intelligence from moving image streams.
Structured + Unstructured
Database records and narrative text reasoned together. The complete enterprise picture.
Cross-Modal Fusion
Each modality enriching every other. The highest tier of multimodal intelligence.
The right model for your use case.
The AI model landscape changes every quarter. New frontier models launch with extraordinary benchmark claims. Open-source alternatives close the gap with commercial models. Smaller, specialised models outperform larger general ones on specific domain tasks at a fraction of the cost. Navigating this without a structured evaluation framework leads to expensive decisions made on marketing rather than measured performance.
At IBaseIT, we run rigorous model evaluation programmes against your actual domain data and production requirements not generic benchmarks that tell you nothing about how a model will perform on your specific tasks. Every shortlisted model is measured across the four dimensions that determine enterprise fitness: accuracy, latency, total cost of ownership, and compliance alignment. The result is a selection decision your board can defend and your engineering team can build on.
Domain-specific benchmarking on your data
We evaluate models against your actual inputs and expected outputs not MMLU, HumanEval, or any other generic benchmark. The only performance that matters is performance on your tasks, measured under your production conditions.
Total cost of ownership modelling
Model selection is an economics decision as much as a technical one. We model inference cost, fine-tuning cost, hosting cost, and maintenance overhead across all candidate models identifying the best value for your use case at your production volume.
Compliance and regulatory assessment
Every candidate model assessed against your data residency requirements, model provenance obligations, licence terms, and explainability capability before performance evaluation begins. Non-compliant candidates are removed before engineering time is invested in evaluating them.
Build vs buy vs fine-tune decision framework
We bring objectivity to the decision every AI programme must make: use a general model as-is, fine-tune an existing model on domain data, or build a specialised model from the ground up. Our 14+ years of delivery experience means this decision is made with evidence, not preference.
How to train, Build and Evaluate AI, Simplified for You.
Most commonly asked questions about model fine-tuning, domain AI, multimodal systems, and model evaluation.
Four accelerators. Better models. Faster delivery.
IBaseIT Tech's four proprietary accelerators compress the model training and evaluation lifecycle embedding quality at every stage and connecting model development to the broader IBaseONE Framework that delivers AI on the Move.
Cortex
Embedded in your AI engineering workflow enforcing model development standards, surfacing reusable fine-tuning components, and accelerating the path from experiment to production-grade model.
QATTs
Continuous model quality testing validating accuracy, fairness, and output consistency across every training run so what ships to production performs as the evaluation confirmed.
IBFORGE
Unlocks legacy data assets for fine-tuning by autonomously extracting, cleaning, and structuring historical enterprise data that would otherwise be inaccessible as training material.
AI Growth Engine
Determines which model investments deliver the highest ROI before engineering resources are committed connecting the build decision to the business case your leadership needs to approve it.
Outcomes across our Train, Build & Evaluate practice.
Bring your hardest problem.
We will bring you
a scalable solution.
Our first conversation is a strategic consultation not a sales call. We listen to your use case, your data reality, and your production requirements. Then we show you the fastest path from model selection to measurable business value.