Most businesses have the data. What they lack is the intelligence layer that turns raw information into decisions, predictions, and automation. Perimattic designs and builds custom AI solutions that work in production — from machine learning models to generative AI pipelines.
AI Stack We Build On — TensorFlow, PyTorch, OpenAI, LangChain, Hugging Face, and the wider modern AI ecosystem
AI development is the process of designing, training, and deploying machine learning models and intelligent systems that can make predictions, recognise patterns, generate content, or automate complex tasks based on your business data. It covers a wide spectrum of capabilities: from supervised classification models that flag fraudulent transactions, to computer vision systems that detect manufacturing defects, to generative AI pipelines that synthesise documents from enterprise knowledge bases.
The challenge for most enterprises is not access to AI models — it is building systems that work reliably with your specific data, integrate with your existing infrastructure, and produce outcomes that can be measured and improved over time. A generic AI tool trained on general internet data will not understand your domain terminology, your customer behaviour patterns, or your operational constraints. The accuracy gap between a generic model and a custom-trained one is material at production scale.
Perimattic builds custom AI solutions from the ground up: machine learning models trained on your data, NLP pipelines tuned to your language and document types, computer vision systems calibrated to your environment, and generative AI architectures grounded in your knowledge base. Every solution is designed for production scale and built with the monitoring and retraining infrastructure needed to keep it accurate as your business evolves.
Training data
Trained on generic internet or public data
Training data
Trained on your specific business data
Domain fit
Generic, not industry or company-specific
Domain fit
Tuned to your domain, terminology, and context
Integration
Limited to vendor-defined outputs and APIs
Integration
Built to integrate with your ERP, CRM, and APIs
Accuracy over time
Degrades as your business data changes
Accuracy over time
Improves as the model is retrained on new data
Ownership
Vendor controls the model, weights, and roadmap
Ownership
You own the model, weights, and all outputs
The difference matters most in high-stakes decisions: credit risk, medical triage, fraud detection, quality inspection. These are exactly where the accuracy gap between generic and custom AI determines whether the system is deployable at all.
Twenty-one specialist service lines covering every layer of enterprise AI — from strategy and data to agents, MLOps, and responsible AI.
A structured six-stage process from free scoping session to live deployment and ongoing model improvement.
We analyse your data landscape, business objectives, and existing infrastructure to identify the AI use cases that will deliver the fastest measurable return. This session is free and carries no obligation.
We evaluate your data quality, availability, and labelling requirements, then design an AI architecture that maps to your use case, infrastructure, compliance constraints, and target performance metrics.
We build a working prototype against a real slice of your data to validate the AI approach and surface any data quality or integration challenges before the full production build begins.
We develop, train, and tune the AI models to your data and target metrics. Feature engineering, hyperparameter optimisation, cross-validation, and evaluation against held-out test sets are all included.
We evaluate model performance against agreed baselines, integrate the model into your application or infrastructure, and run end-to-end testing across production scenarios including edge cases and adversarial inputs.
We deploy to your infrastructure with monitoring dashboards, drift detection, and retraining pipelines. We remain available for the first weeks in production, resolve any issues, and plan the next model iteration.
We analyse your data landscape, business objectives, and existing infrastructure to identify the AI use cases that will deliver the fastest measurable return. This session is free and carries no obligation.
We evaluate your data quality, availability, and labelling requirements, then design an AI architecture that maps to your use case, infrastructure, compliance constraints, and target performance metrics.
We build a working prototype against a real slice of your data to validate the AI approach and surface any data quality or integration challenges before the full production build begins.
We develop, train, and tune the AI models to your data and target metrics. Feature engineering, hyperparameter optimisation, cross-validation, and evaluation against held-out test sets are all included.
We evaluate model performance against agreed baselines, integrate the model into your application or infrastructure, and run end-to-end testing across production scenarios including edge cases and adversarial inputs.
We deploy to your infrastructure with monitoring dashboards, drift detection, and retraining pipelines. We remain available for the first weeks in production, resolve any issues, and plan the next model iteration.
Select a function to see how custom AI models reduce manual workload and improve outcomes in that domain.
AI models transform unstructured clinical data into structured insight, reducing documentation burden and improving diagnostic and operational accuracy.
AI models transform unstructured clinical data into structured insight, reducing documentation burden and improving diagnostic and operational accuracy.
AI models reduce manual review, flag risk earlier, and improve decision quality across lending, underwriting, fraud operations, and compliance.
AI systems monitor equipment, predict failures, and optimise production throughput by learning from sensor and operational data over time.
Retail AI improves conversion, reduces returns, and personalises experiences at scale by modelling shopper intent and product affinity from behavioural data.
AI models reduce screening time and improve shortlist quality by learning from historical hiring data and applying consistent evaluation criteria at scale.
NLP and classification models reduce handle time, improve routing accuracy, and surface relevant knowledge to agents and automated systems alike.
Perimattic's work resulted in stable production systems. The team was helpful, easily accessible, and communicative through email. Their professionalism was impressive.
Quality
4.5
Schedule
5.0
Cost
5.0
Willing to Refer
4.5
DevOps Managed Services · Oct 2023 – Aug 2024
24/7 monitoring and support for production environments plus Linux server administration for a leasing automation company.
The new architecture is scalable and highly efficient, saving a lot of money in fees. Perimattic provides high-quality IT consulting and cloud development work promptly and at great value. The team remains involved from the planning stage to providing support, showing diligence and proactiveness.
Quality
5.0
Schedule
5.0
Cost
4.5
Willing to Refer
5.0
AWS Migration (Legacy → Microservices) · Nov 2018 – Ongoing
Transitioned a travel systems company's legacy server system to an AWS-based microservices architecture with ongoing maintenance.
Four structural advantages that separate production-grade AI engineering from prototype-quality builds.
Every AI solution we build includes data validation pipelines, model monitoring, drift detection, fallback logic, and observability from the first sprint. We do not build prototypes that need to be re-engineered before they can go live.
We build models trained on your data, not wrappers around generic APIs. You get a solution that understands your domain, improves as your data grows, and is not subject to upstream vendor changes or unpredictable cost increases.
Our team covers the full AI development stack: data engineering, model development, MLOps, and application integration. You get a single team responsible for everything from data pipeline to deployed production API.
The team that designs your AI architecture also builds, tests, and deploys it. You get continuity of context from scoping call to go-live, with no hand-off loss between advisory and engineering functions.
“Unlike boutique dev shops that build to spec without strategic context, or large consultancies that advise without shipping, Perimattic brings both capabilities to every engagement.”
Custom AI development is the process of designing, training, and deploying machine learning models and intelligent systems built specifically for your business data, workflows, and objectives. Unlike off-the-shelf AI tools trained on generic data, a custom AI solution learns from your historical data, understands your domain, and integrates with the systems your business already uses.
Off-the-shelf AI tools are trained on general data and designed to work for many use cases at an average level of accuracy. Custom AI development produces a model trained specifically on your data — your transactions, your documents, your customer behaviour — which means it understands your domain, adapts to your edge cases, and improves as your data grows. For business-critical decisions, the accuracy and reliability difference is significant.
We build across the full spectrum of applied AI: supervised learning models for classification and regression, unsupervised models for clustering and anomaly detection, NLP pipelines for text and document intelligence, computer vision systems for image and video analysis, generative AI and RAG pipelines for content and knowledge retrieval, and AI agents for autonomous workflow automation. The right type depends on your data, use case, and target outcome.
A proof-of-concept model for a well-scoped use case typically takes two to four weeks. A production model with full integration, monitoring, and retraining infrastructure typically takes six to fourteen weeks depending on data readiness and integration complexity. Generative AI and RAG systems with large document corpora can take longer due to data preparation requirements. We provide a more accurate estimate after the free scoping session.
A focused single-use-case AI model starts from around USD 12,000. A full production AI system covering multiple functions — with data pipelines, model training, API integration, and monitoring — is typically USD 35,000 to USD 100,000. Generative AI systems with large RAG corpora or multi-model architectures vary based on data scope. We scope every engagement before quoting so there are no surprises.
The data requirements depend on the type of model. Supervised learning models need labelled historical examples — typically hundreds to thousands of examples for simpler tasks, tens of thousands for more complex ones. NLP and computer vision models need domain-specific text or images. Generative AI and RAG systems need your knowledge base in a retrievable format. We assess your data readiness in the free scoping session and identify any gaps before committing to a build.
We establish evaluation metrics and baseline targets before development begins, so performance is measured objectively rather than subjectively. Depending on the model type, we use precision, recall, F1 score, mean absolute error, BLEU score, or business-specific metrics like processing time reduction or approval accuracy. We hold out a test set from the training process and validate against it separately. We also run production monitoring so you have ongoing visibility of model accuracy over time.
Yes. We have deep experience connecting AI models to ERPNext, Salesforce, HubSpot, SAP, and custom REST APIs. For ERPNext specifically, we can trigger model inference from ERP events, write model outputs back into ERP records, and surface AI-driven recommendations within the ERP interface. We handle authentication, rate limiting, data validation, and fallback logic as part of every integration.
Machine learning is a broad category of algorithms that learn patterns from data to make predictions or decisions, including decision trees, random forests, gradient boosting, and linear models. Deep learning is a subset of machine learning that uses multi-layered neural networks and is particularly powerful for unstructured data like images, audio, and text. We use both, choosing the approach based on your data type, volume, and the interpretability requirements of your use case.
We have delivered AI projects for clients in financial services, healthcare, insurance, manufacturing, logistics, retail, real estate, and professional services. Our processes and integration patterns are designed to meet the data sensitivity and compliance requirements common in these sectors. We can discuss industry-specific requirements on the free scoping call.
Tell us what you want to predict, automate, or understand better. We will show you exactly how custom AI can reduce manual workload, improve decision quality, and deliver a measurable return in the first quarter.