Data pipelineSources clean
Custom AI Development Company
Custom AI development for software that has to work.
Inversify Media builds the model layer, data pipeline, interface, and review path together so AI can live inside apps, CRMs, dashboards, and operations instead of sitting off to the side as a demo.
- AI apps, agents, and software integration
- Machine learning, deep learning, RAG, and evaluations
- Production deployment with review paths, logging, and security
Models, agents, and apps
We scope the model, software interface, workflow logic, and review path as one system.
Useful when data supports it
Forecasting, classification, scoring, recommendations, and anomaly detection belong inside working software, not isolated notebooks.
Deep learning when warranted
Vision, audio, document intelligence, generation, and high-dimensional pattern work get deeper modeling only when the problem actually needs it.
AI Development Scope
Build the model layer, then make it usable.
Useful AI is rarely one trick. It is the right model path, the right software surface, the right data pipeline, and enough evaluation to trust the result in production.
AI Applications
Custom AI apps and product features
AI-powered portals, dashboards, assistants, admin tools, SaaS features, and customer-facing workflows that people can use without leaving the product.
- AI apps
- Dashboards
- SaaS features
Machine Learning
Prediction, scoring, classification, and recommendations
Machine learning for lead scoring, risk flags, demand signals, churn, prioritization, matching, and decision support when the data is strong enough to trust.
- Prediction
- Scoring
- Classification
Deep Learning
Vision, audio, documents, and generation-heavy systems
Deep learning for image and video understanding, speech workflows, document intelligence, generation pipelines, and unstructured inputs that need richer model behavior.
- Vision
- Audio
- Documents
RAG + Knowledge
Retrieval systems grounded in private business data
RAG pipelines, embeddings, vector search, semantic retrieval, citations, source controls, and answers grounded in approved knowledge.
- RAG
- Embeddings
- Search
Agents
AI agents with tools, approvals, and workflow logic
Agent systems that classify, draft, route, summarize, update records, call tools, and pause for approval where the business needs control.
- Tool calling
- Approvals
- Routing
MLOps
Deployment, monitoring, evaluation, and GPU infrastructure
Production AI needs hosting, evaluations, logging, security, rollback paths, latency planning, cost controls, and a deployment path the team can maintain.
- MLOps
- Evals
- GPU deployment
How Buyers Should Compare
Choose the AI partner by fit, not by buzzwords.
Some teams need a research lab. Some need a narrow SaaS tool. Inversify is strongest when AI has to become part of a product, workflow, CRM, database, dashboard, or internal operating system.
Provider typeBest forWatch forWhere Inversify fits
Generic AI automation shopSimple lead response, content drafting, and tool-to-tool automations.May stop at prompts and workflows without the software, data, model, or review architecture needed for production.Strong fit when the AI must live inside a custom app, CRM, dashboard, portal, or owned business system.
Enterprise data science firmLarge institutional datasets, research programs, geospatial analytics, and enterprise modernization.May be heavier than a founder, local operator, nonprofit, or mid-market team needs for a focused launch.Strong fit when you need senior strategy, fast implementation, clean UX, and practical AI connected to daily operations.
Offshore software vendorLarge build teams, staff augmentation, and low-cost execution for tightly specified tasks.Often needs heavy management and may miss positioning, product judgment, data boundaries, and U.S. business context.Strong fit when the scope is still being shaped and the same team needs to own strategy, software, AI, and launch quality.
Niche AI SaaS toolOne narrow workflow, such as a receptionist, chatbot, summarizer, or content generator.Can become limiting when the workflow needs custom records, permissions, dashboards, forms, documents, and follow-up.Strong fit when the AI should become part of a broader system the business owns and can keep improving.
Production Path
From model idea to deployed AI system.
AI development only counts when the model, data, interface, permissions, monitoring, and business outcome hold up for real users.
- 01
Define the AI job and data boundary
We map the workflow, users, data sources, privacy needs, failure cases, model touchpoints, and the decisions that should stay under human review.
- Workflow map
- Data inventory
- Risk boundaries
- 02
Choose the simplest model architecture that can do the job
We decide whether the system needs an LLM, RAG, classic ML, deep learning, fine-tuning, an agent loop, or a simpler rules-plus-model design.
- Model plan
- Evaluation criteria
- Cost/latency target
- 03
Build the app, pipeline, and evaluation layer
We build the software surface, data processing, prompts, retrieval, model logic, APIs, test cases, and review tools around examples from the actual workflow.
- Working prototype
- Evals
- Review interface
- 04
Deploy, monitor, and improve in production
We launch with logging, monitoring, access controls, approval points, feedback loops, and the deployment path the system needs to keep improving.
- Production launch
- Monitoring
- Iteration plan
FAQ
Custom AI development FAQ
- What does a custom AI development company build?
- A custom AI development company builds the AI layer and the software around it: AI applications, machine learning models, deep learning pipelines, RAG systems, AI agents, evaluation tools, and production interfaces connected to real workflows.
- Does Inversify Media build machine learning and deep learning systems?
- Yes. We build machine learning for scoring, forecasting, classification, recommendations, and anomaly detection. We use deep learning for vision, audio, documents, generation, and high-dimensional pattern work when the data and use case justify it.
- Do you build RAG systems and AI agents?
- Yes. We build retrieval-augmented generation systems, semantic search, private knowledge assistants, agent workflows, tool calling, approval paths, and the software interfaces that make those systems usable by real teams.
- When is custom AI better than an off-the-shelf AI tool?
- Custom AI is worth it when the workflow depends on private data, custom records, permissions, domain rules, model evaluation, software integration, human review, or a product experience that a generic tool cannot match. If a simple tool solves the problem cleanly, we will say that.
- Do you build AI from scratch or integrate existing models?
- Both. Many strong systems combine existing model APIs, retrieval, custom prompts, data processing, evaluation, and workflow logic. From-scratch or fine-tuned models make sense when the behavior, data, privacy, or deployment requirements call for it.
- How long does custom AI development take?
- A focused first release usually takes several weeks to a few months, depending on data readiness, integrations, review requirements, and how much evaluation the system needs before launch.
- How much does custom AI development cost?
- Most serious AI builds should be scoped in phases. Cost depends on the model approach, data cleanup, integrations, interface, security needs, evaluation, and deployment. We define a fixed first phase so the project has a clear number before development starts.
- Can you deploy and maintain production AI systems?
- Yes. Production AI can include model hosting, GPU infrastructure, monitoring, logging, cost controls, security boundaries, human review, evaluation sets, and iteration after launch.
Custom AI Development Brief
Tell us what AI system you need to build.
Share the product, workflow, data sources, model needs, review requirements, and where the AI has to run. We can scope the right first phase.