AI DEVELOPMENT SERVICES
We build production AI that actually ships.
From RAG pipelines and private LLMs to autonomous agents and vibe-coded delivery — Aanandi Technosoft builds the full modern AI stack. Reliable, observable, and measurably better than the demo.
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.
From RAG pipelines and private LLMs to autonomous agents and vibe-coded delivery — Aanandi Technosoft builds the full modern AI stack. Reliable, observable, and measurably better than the demo.
Six pillars of modern AI engineering.
LLM Applications
Customer-facing chatbots, copilots, content tools and assistants — built on the right model for the job (GPT, Claude, Gemini, Llama).
RAG & Knowledge Systems
Connect LLMs to your data — retrieval-augmented generation with hybrid search, reranking, and citation-grade answers.
AI Agents & Automation
Multi-step agents that browse, code, call tools and complete real work — Lang Graph, Crew AI, Auto Gen, custom orchestration.
Private & On-Prem LLMs
Self-hosted Llama/Mistral/Qwen on your infrastructure. Full data control, no third-party API spend, SOC 2-friendly.
MLOps & Evaluation
Continuous evals, observability, guardrails, prompt versioning and CI for AI systems — so models stay reliable in production.
Computer Vision & Multimodal
Image, video and document intelligence. Detection, segmentation, OCR, generative imagery, vision-language models.
Every layer of an AI system.
Modern AI is rarely one model — it's models, retrieval, evals, infra, guardrails, and UX working together. We build all of it.
Generative AI & LLMs
Retrieval & Knowledge
AI Agents & Workflow
Private & Self-Hosted AI
MLOps & Production
Computer Vision
NLP, Speech & Audio
Data, ML & Strategy
- ✓Custom LLM application development
- ✓Prompt engineering & optimization
- ✓LLM fine-tuning (Lo — RA, QLo RA, full SFT)
- ✓RLHF / DPO alignment
- ✓Custom GPTs and Open AI Assistants
- ✓Multimodal apps (text + vision + audio)
- ✓Long-context document processing
- ✓RAG pipeline architecture
- ✓Vector database setup — Pinecone, Weaviate, Qdrant, Chroma, Milvus, pgvector
- ✓Embeddings & semantic search
- ✓Hybrid search (BM 25 + dense)
- ✓Reranking with cross-encoders
- ✓Knowledge graph integration
- ✓Document extraction & OCR pipelines
- ✓Citation-grounded answer systems
- ✓Lang Chain & Lang Graph agents
- ✓Crew AI / Auto — Gen multi-agent systems
- ✓Tool use & function calling
- ✓Browser-using agents
- ✓Voice agents (Live — Kit, Vapi, Retell)
- ✓Customer support copilots
- ✓Sales & Rev Ops automation
- ✓Custom & private LLM development services
- ✓On-prem Llama, Mistral, Qwen, Deep — Seek deployments
- ✓Air-gapped & SOC 2/HIPAA-friendly setups
- ✓v LLM, TGI, llama.cpp, Ollama serving
- ✓GPU infrastructure (H 100, A 100, L 40 S)
- ✓Model quantization (GPTQ, AWQ, GGUF)
- ✓Inference optimization (Tensor RT-LLM)
- ✓Multi-tenant model gateways
- ✓BYOC private cloud LLMs
- ✓LLM observability (Langfuse, Arize, Lang Smith)
- ✓Continuous evaluation harnesses
- ✓Guardrails & safety filters
- ✓Prompt versioning & A/B testing
- ✓AI gateways (Lite — LLM, Portkey, custom)
- ✓Cost & latency monitoring
- ✓Red-teaming & adversarial testing
- ✓CI/CD for AI systems
- ✓Object detection & segmentation
- ✓OCR & document AI
- ✓Video understanding & action recognition
- ✓Generative imagery (SDXL, Flux, Imagen)
- ✓Vision-language models (CLIP, BLIP, Florence)
- ✓Pose estimation & tracking
- ✓3 D reconstruction & Ne RFs
- ✓Edge vision deployments
- ✓Sentiment & intent classification
- ✓Named entity recognition
- ✓Summarization & translation
- ✓Speech-to-text (Whisper, Deepgram)
- ✓Text-to-speech (Eleven — Labs, Open AI TTS)
- ✓Speaker diarization
- ✓Real-time audio pipelines
- ✓Conversational search
- ✓Synthetic data generation
- ✓Recommendation engines
- ✓Forecasting & time-series
- ✓Anomaly & fraud detection
- ✓Classical ML & gradient boosting
- ✓AI readiness audits
- ✓Model selection consulting
- ✓AI strategy & roadmap
From spike to scale in six steps.
Discover
Design
System architecture, eval criteria, guardrails, scope.
Prototype
Working spike on real data in 2–4 weeks.
Evaluate
Golden datasets, A/B testing, human-in-the-loop QA.
Deploy
Production rollout with observability and rollbacks.
Scale
Optimization, fine-tuning, expansion to new use cases.
Fluent in the modern AI stack.
We pick the right tool — not the trendy one. Below is the slice of the ecosystem we deploy most often.
AI shipped across regulated industries.
Healthcare
Clinical documentation copilots, prior auth automation, HIPAA-grade chat.
Financial Services
Document intelligence, KYC/AML automation, advisor copilots.
Legal
Contract review, discovery, citation-grounded research assistants.
E-Commerce
Product search, generative merchandising, AI customer support.
Saa S
Embedded copilots, AI-native onboarding, in-app agents.
Manufacturing
Vision QA, predictive maintenance, RAG for technical manuals.
Education
Personalized tutors, content generation, assessment automation.
Media
Generative imagery, video tooling, content moderation at scale. Free 30-minute scoping call. We'll review your use case, recommend a stack, and outline a realistic plan to ship.
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
What does vibe coding actually mean?+
It's the discipline of building software hand-in-glove with AI — pairing engineers with code-generation models, agentic tooling, and rapid iteration loops to ship features dramatically faster than traditional development.
Should we use a private LLM or a hosted API?+
Hosted APIs (OpenAI, Anthropic) win on capability and speed-to-launch. Private LLMs (Llama, Mistral) win on data sovereignty, unit cost at high volume, and predictable latency. We help you pick the right call — and many production systems use both.
How long does a RAG implementation take?+
A working prototype on real data: 2–4 weeks. Production-ready with evals, reranking, and observability: 6–12 weeks. Most of the work is data prep and eval — not the LLM itself.
Do you handle fine-tuning?+
Yes — LoRA/QLoRA for adapting open models, full SFT when needed, and DPO/RLHF alignment. We also tell clients when fine-tuning isn't the right answer (usually: try a better prompt or RAG first).
Which vector database should we use?+
Depends on scale, latency, and existing stack. pgvector is great when you're already on Postgres. Qdrant and Weaviate excel for dedicated workloads. Pinecone for fully managed. Milvus for very large scale. We benchmark for your case.
Can you build AI agents that actually work?+
Yes — but agent reliability comes from constrained tool use, evals, and human-in-the-loop fallbacks, not from longer prompts. We design agents for measurable success rates, not demos.