Enterprise AI Development & Generative AI Consulting
Custom AI Software Development
Your working knowledge sits across documents and systems. An assistant needs current information and permission to use it.
Teams switch between tools to complete one process. Connecting the steps also means deciding who can act and who handles exceptions.
An AI feature helps with a draft, but users need to know when to review it, reject it or fall back to ordinary software.
After launch, someone must maintain prompts, source context, integrations and cost controls as the work changes.
Generative AI Consulting Services
Decision work and delivery stay connected. The team that validates the use case carries the evaluation plan, cost assumptions, and failure boundaries into implementation.
Intelligence
Defined scope · Written decision record
Figure out whether the AI initiative should proceed, and how.
AI Readiness & Feasibility
Determine whether your AI initiative can work in production, not just in a demo.
Use Case Validation
Identify which AI ideas are worth building and which ones should wait.
Data Readiness Assessment
Evaluate whether your data is clean, connected, and governed enough for AI.
AI Cost & ROI Modeling
Model what your AI system will cost at scale and whether the business case holds.
Execution
Milestone-based · Working system and release evidence
Build AI systems that work under real constraints.
Custom AI Workforce
Build and manage coordinated AI roles around your systems, approval rules and recurring work.
LLM & RAG Systems
Build knowledge assistants and document search with governed retrieval, source references and evaluated answers.
Agentic Workflows
Connect multi-step work across systems, with scoped actions, approval rules, exception handling and recovery.
AI Integration into Products
Add copilots, AI search and task assistance to your existing product, with clear access and user-experience boundaries.
One accountable delivery context from validation through production evidence.
Production evidence
Release criteria
Quality, safety, latency, cost, and fallback thresholds defined before production release.
Guardrail evidence
Versioned evaluations, policy checks, escalation paths, and production feedback tied to each release.
Cost envelope
Usage assumptions, model routing, budgets, and exception handling made visible for review.
Where Bedrock and copilots fit
Choose the service around the work you need done. Then choose the platform, interface and operating model that fit it.
Amazon Bedrock within your AWS implementation
Bedrock can sit within an LLM and RAG system, an agentic workflow or the model layer of a custom AI workforce. We assess model access, data permissions, evaluation and operating costs for the workload, rather than treating a platform choice as the whole solution.
Explore Amazon Bedrock implementationCopilots within your product or workflow
An embedded copilot belongs within AI Integration into Products. An assistant that coordinates steps across tools fits Agentic Workflows. For recurring responsibilities across your business, Custom AI Workforce adds defined roles and ongoing management.
Explore copilot and automation patternsEvaluation, observability, permission controls and operating costs are part of delivery across these four services. They are not a fifth service you need to choose first.
How we evaluate and monitor AI systems Read the full AI workforce service scopeChoose the AI development work your workflow actually needs
Enterprise AI development services begin with a useful task, the data it may access, and the consequence of a wrong result. The implementation then follows those constraints.
AI software development and product integration
Connect an AI capability to identity, application permissions, domain records, user experience, and operating telemetry. Decide when to answer, abstain, or request approval. A working demonstration becomes a production feature only when the team can evaluate quality, contain failure, control cost, and own changes to the underlying model or data.
AI product integration servicesGenerative AI, retrieval, or an agent?
Use retrieval when answers need approved company information and traceable sources. Use a bounded agent when a workflow requires tools, state, and explicit authority to act. A fixed workflow or conventional software may be preferable when the task is deterministic. Compare these options before committing to an agent or model-training project.
AI agent and workflow developmentFrom prototype to production evidence
Establish a representative evaluation set, data permissions, latency and cost budgets, and failure behavior before broad rollout. Release to a bounded use case, monitor changes in retrieval or model behavior, and retain a fallback. Prototype, integration, evaluation, and ongoing operation should have separately agreed scope and acceptance criteria.
AI evaluation and monitoringFrequently asked questions
What AI development services does Ampity offer?
Our four main AI services are Custom AI Workforce, LLM and RAG Systems, Agentic Workflows, and AI Integration into Products. Readiness, data and cost assessments help scope the work. Amazon Bedrock implementations and copilots sit within these services, with evaluation and operating controls agreed for the workload.
How do you choose the right AI use case?
We assess business value, data availability, risk and feasibility, then validate the strongest candidates with a focused prototype before committing to a full build.
Do you build custom AI or use existing models?
Usually both. Most solutions combine proven foundation models with your own data, workflows and guardrails rather than training models from scratch.
How do you keep enterprise AI secure?
Through access controls, data handling rules, private deployment options, guardrails, audit logging and evaluation before and after release.
How do you move an AI prototype into production?
By adding evaluation, monitoring, cost controls, security review, integration with existing systems and clear operational ownership.