The AI development company enterprises trust to build what actually ships

An AI development company designs, builds, and deploys custom artificial intelligence systems for business use. Unlike an AI consultancy that only advises, an AI development company owns the full engineering lifecycle: data pipelines, model selection, integration, MLOps, and production support. Perimattic is an AI development company serving enterprises across the United States and United Kingdom.

Custom AI development projects typically range from $5,000 for a scoped proof of concept to $250,000+ for a full production system. Most enterprise AI builds start at $5,000, depending on data readiness, model complexity, and integration surface. We provide fixed-scope estimates after a discovery call.

A production-ready AI system usually takes 8 to 20 weeks end to end. A scoped proof of concept can ship in 2 to 4 weeks. Timelines depend on data availability, integration complexity, and whether the model is built on a hosted foundation model (GPT-4, Claude, Gemini) or custom-trained. Perimattic runs weekly sprints with working demos at each milestone.

AI consulting produces a strategy, roadmap, and use-case prioritization - no code. AI development is the engineering work that turns that roadmap into a running system: data engineering, model training, application code, deployment, and monitoring. Perimattic offers both, but our core practice is AI development - we ship systems, not slide decks.

Perimattic delivers AI systems across six verticals: banking, financial services, healthcare, manufacturing, logistics, and travel. Each vertical has domain-specific engineers who understand the data patterns, regulatory constraints, and operational realities of that industry - for example, HIPAA-aware pipelines in healthcare and PCI-DSS-aware data handling in financial services.

Perimattic builds on GPT-4, Claude, Gemini, and open-source models (Llama, Mistral) via frameworks like LangChain and LlamaIndex. Our stack covers vector databases (Pinecone, Weaviate, pgvector), MLOps (MLflow, Weights & Biases), cloud AI services on AWS and Azure, and full-stack integration with Next.js, Node.js, and Python. We select the stack based on your production constraints, not vendor preference.

Evaluate on three dimensions: production track record (ask for shipped systems, not pilots), team depth (data engineers, MLOps engineers, and application developers under one roof), and delivery transparency (sprint cadence, working demos, clear pricing). Ask for verifiable client references on Clutch or G2. Avoid firms that lead with model demos instead of production systems.

You own everything: the code, the models, the weights, and the data. It is written into the contract from the first commit. Your data is used solely to build and evaluate your system - it never trains a shared model, never leaves the environment you approve, and is never reused on another engagement. Where we build on a hosted foundation model, we use the enterprise tiers with training opt-out enabled by default, and we document which provider processes what before any data moves.

Yes - integration is usually the larger half of the project. We connect to Salesforce, HubSpot, SAP, Dynamics, NetSuite, ServiceNow, and ERPNext through their APIs, and to legacy systems through whatever they expose: REST, SOAP, message queues, database replicas, or scheduled file drops. Where an older system exposes nothing, an integration layer sits alongside it rather than modifying it. Integration surface is the single biggest driver of timeline, so we map it during discovery before quoting.

It depends on the approach. A RAG or retrieval system works from documents you already have - hundreds of files is often enough to be useful. Fine-tuning a language model needs a few hundred to a few thousand labelled examples. A supervised ML model for prediction or classification typically needs thousands of historical records with known outcomes. If you do not have enough data yet, the first phase is usually instrumentation: capturing the data properly so the model becomes possible in six months rather than never.

AI systems drift as the world changes around them, so maintenance is scoped into the engagement rather than treated as an afterthought. Post-launch support runs as a monthly retainer covering model performance monitoring, retraining when accuracy degrades, prompt and pipeline updates as foundation models change, and infrastructure upkeep. Typical retainers run from a few hundred to a few thousand dollars a month depending on system complexity and SLA. Full documentation and runbooks transfer at handover either way, so an in-house team can take it over instead.

Three: fixed-scope for well-defined builds where the requirements are clear and the price is agreed up front; time and materials for exploratory work where scope will genuinely move; and dedicated team where engineers embed with your team for a sustained period. Most enterprise AI builds start fixed-scope for a proof of concept, then move to a dedicated team once the direction is proven. We quote fixed-scope wherever the requirements support it, because it puts the estimation risk on us rather than you.