AI APPLICATION DEVELOPMENT
Custom AI applications, end to end.
Customer-facing AI products, internal copilots, AI-native SaaS. We design, build, and deploy AI applications that ship — and stay shipped — with the architecture, observability, and ownership you'd expect from software you'd actually rely on.
AI-ready in weeks · Book a free AI readiness call
Enterprise-grade delivery. Human-verified outcomes.
Built for teams that can't afford to guess.
Customer-facing AI products, internal copilots, AI-native Saa S. We design, build, and deploy AI applications that ship — and stay shipped — with the architecture, observability, and ownership you'd expect from software you'd actually rely on.
Five categories of AI applications we build.
Chat & assistants
Conversational interfaces grounded in your data, brand voice, and product context.
Copilots & in-product AI
AI features inside an existing product surface, accelerating the user's primary task.
Agents & autonomous workflows
Multi-step systems that complete operational work end to end.
Generative tools
Product features that produce content — text, image, code, structured data — on demand.
Embedded AI features
AI capabilities added to your existing product. Lowest scope, often highest impact.
Semantic, citation-grounded search across your content corpus.
Most AI apps are made of these building blocks.
A typical project picks 2–4 and combines them. We have dedicated pages on each.
Retrieval (RAG)
Grounds answers in your documents, with citations and evals.
Agents
Multi-step systems that use tools to complete work autonomously.
Private LLMs
Self-hosted models for sovereignty, compliance, or cost.
Workflow automation
Operational automations with humans in the loop.
Vector & search
Semantic search, hybrid retrieval, citation grounding.
Document intelligence
Parsing, OCR, extraction from messy real-world documents.
From idea to live AI app in six steps.
Discover
Use case audit, success criteria, model selection, build-vs-buy assessment.
Design
Architecture, data flow, UX, evaluation framework, security posture.
Prototype
Evaluate
Golden dataset, eval harness, A/B testing, human-in-the-loop QA.
Deploy
Production rollout with observability, rollback plan, monitoring dashboards.
Scale
Continuous optimization, fine-tuning, capability expansion, model migrations.
How AI application projects engage with us.
Tell us about the AI app you want to build.
A 30-minute call. We'll talk through what you're trying to accomplish, what's been tried, and what the right next step is — Discovery, Prototype, or straight to Production.
Semantic, citation-grounded search across your content corpus.
Ways to engage.
Ready to build?
Let's build your next intelligent platform.
Share your goals — we'll recommend a model, timeline, and team that fits Aanandi Technosoft.
Frequently asked questions
AI app vs. regular app with AI features?+
An AI app's primary value comes from the AI — chat, generation, agents, search. A regular app with AI features has AI augmenting an existing workflow. AI-primary apps need bigger eval and observability investment.
Hosted models or private LLMs?+
Mostly hosted to start — fastest path to validation. Move workloads to private LLMs when data sovereignty, cost at scale, or latency justify it, usually after 6–12 months.
How long does a build take?+
Embedded feature: 4–8 weeks. Customer-facing product: 8–16 weeks. Multi-feature platform: 12–24 weeks.
Will I own the code?+
Yes, 100%. Source in your repository, infrastructure in your cloud accounts.
How do you handle evals?+
Every project gets a golden dataset scored on every release with LLM-as-judge plus structured assertions. Regression in CI blocks deploy.
What about hallucinations?+
Depends on app type. RAG: citation grounding + verification. Agents: confidence thresholds + human-in-the-loop. Chat: refusal training + safety prompts + guardrails.