01
AI application architecture
Model interfaces, data flow, retrieval, permissions, backend services, frontend behavior, and deployment topology.
Service 03
AI software development services for production systems
AI products, integrations, and systems built for production.
Aatvi builds AI software that connects models to real products, private data, workflows, and operating constraints. We cover frontend, backend, LLM integration, RAG, agents, evals, observability, security, and deployment so the system can be used beyond a demo.
Provider-agnostic AI implementation across model APIs, retrieval, data stores, and app infrastructure.
Evaluation, monitoring, permissions, fallback paths, and cost controls planned before launch.
Full-stack engineering for the user experience, backend, data flow, and deployment surface.
What this includes
Deliverables a buyer can inspect.
Every service page is written around concrete artifacts. The work should be easy to evaluate before, during, and after the engagement.
02
LLM/RAG implementation
Grounded retrieval, document ingestion, chunking, ranking, citations, prompts, and response controls where the use case needs them.
03
Evals and observability
Test sets, acceptance checks, logging, traces, quality review, cost monitoring, and failure investigation paths.
04
Production handoff
Documentation, runbooks, deployment notes, security considerations, and a backlog for future improvement.
Risk reduction
What this service is designed to prevent.
Good AI services are not just capability lists. They reduce specific failure modes that buyers already feel.
Unreliable answers
Without retrieval design, evals, and source constraints, LLM features can sound confident while being wrong.
Integration fragility
AI features often fail at the seams between product UX, backend systems, permissions, and private data.
Unbounded cost and latency
A useful AI product needs token, retrieval, model, caching, and fallback decisions designed into the architecture.
Field guides
Read the supporting playbooks.
Process
How Aatvi delivers ai software development.
Define the job
We identify the user, workflow, data, risks, and acceptance criteria before choosing AI architecture.
Design the system
We map model calls, retrieval, tools, permissions, storage, UX states, and fallback behavior.
Build the slice
We ship the narrowest working system with tests, evals, logs, deployment path, and review loops.
Harden from evidence
Usage, eval failures, cost, latency, and support signals decide what gets improved next.
Who this is for
A product team needs an AI feature that works inside an existing application.
An enterprise wants a private-data assistant with citations, access boundaries, and evals.
A startup needs an AI-native product built with reliable architecture from the start.
A team has a proof of concept and needs production engineering around it.
Who this is not for
Toy demos that will never connect to real users, data, or workflows.
Undifferentiated chatbot work where accuracy, permissions, and operating model do not matter.
Projects that require a model choice before the problem and data are understood.
Comparison
How this differs from a generic service engagement.
Concern
Generic approach
Aatvi approach
Model choice
Pick the trendiest model first.
Choose models after workflow, data, latency, cost, and risk are understood.
Quality
Demo until the happy path looks good.
Use evals, logs, fallback states, source citations, and human review where needed.
Ownership
Ship a black box integration.
Leave architecture, runbooks, and decision records the team can maintain.
Frequently asked
Common questions about ai software development.
What kinds of AI software do you build?
We build LLM applications, RAG systems, AI copilots, internal automation tools, AI product features, workflow agents, and private-data assistants.
Do you work with OpenAI, Anthropic, Gemini, and open models?
Yes. We are provider-agnostic and choose the model stack based on accuracy, latency, cost, privacy, deployment constraints, and maintainability.
Can you add AI to an existing product?
Yes. We often integrate AI into existing products through search, summarization, automation, recommendations, copilots, or knowledge workflows without rewriting the whole application.
Do you include evals and monitoring?
Yes. AI software is not production-ready without a way to evaluate quality, investigate failures, monitor cost and latency, and improve behavior over time.
Related services
Other paths that may fit the problem.
Next step
Bring the problem closest to launch pressure.
We will help decide whether the right first step is an audit, roadmap, build sprint, design sprint, or a narrower technical review.