AI Development Services
Enterprise AI development services for custom software and workflows. Move from use-case assessment to integration, evaluation and monitored production systems.
The distance between a working demo and a working production system is where most AI initiatives lose momentum, budget, and executive confidence.
The prototype ran on clean data. Production data is messy, inconsistent, and ten times the volume.
Latency was fine with ten users. With ten thousand, the inference pipeline is too slow and too expensive.
The model's answers were impressive in testing. In production, hallucinations happen at the worst possible moments, with no monitoring to catch them.
The board approved the budget based on the demo. Six months in, costs are three times the projection and accuracy is lower than expected. The original technical decision is being questioned.
Validate the bet before committing. Then build it with the team that validated it.
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.
Amazon Bedrock Consulting
Evaluate and implement an AWS AI workflow with scoped data access, release evidence and operating ownership.
LLM & RAG Systems
Build production LLM applications grounded in your data with retrieval-augmented generation.
Agentic Workflows
Deploy autonomous AI agents that handle complex, multi-step tasks reliably.
AI Copilots & Automation
Build copilots and intelligent automation that reduce manual work across workflows.
AI Integration into Products
Embed AI capabilities into your existing products without rebuilding them.
LLM Evaluation & Observability
Define release evidence, guardrails, and production quality signals for LLM, RAG, and agentic systems.
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.
Choose 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?
AI strategy and use case validation, generative AI and LLM applications, RAG systems, AI agents, copilots, workflow automation and production monitoring of AI systems.
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.