[ The gap is rarely the model ]
Most generative AI projects demo brilliantly and then stall. The gap is rarely the model — it is retrieval quality, evaluation, cost per query, and what happens when the system is confidently wrong in front of a customer. Techtaru Digital is a generative AI development company that builds for that second phase: LLM applications, RAG systems, fine-tuned models and enterprise generative AI with the evaluation harness attached.
Most generative AI projects demo brilliantly and then stall. The gap is rarely the model — it is retrieval quality, evaluation, cost per query, and what happens when the system is confidently wrong in front of a customer. Techtaru Digital is a generative AI development company that builds for that second phase: LLM applications, RAG systems, fine-tuned models and enterprise generative AI with the evaluation harness attached.
Tell us the process you want to improve and we will assess feasibility before you commit to a buildend-to-end delivery from use-case selection through production deployment and monitoring.
applications built on GPT, Claude, Gemini, Llama and open-weight models, with routing between them by task and cost.
retrieval-augmented generation over your own documents, databases and knowledge bases.
supervised fine-tuning and LoRA adapters where prompting and retrieval have genuinely hit their limit.
use-case prioritisation by value and feasibility, build-versus-buy analysis and honest feasibility assessment.
deployment inside your security boundary with access control, audit logging and data residency.
document processing, summarisation, classification and drafting embedded into existing business processes.
Asked
What is the refund window on a bulk order?
Retrieved from your documents
Answered
Bulk orders over ₹50,000 can be returned
within 21 days of delivery, against the
standard 7 days. The goods must be unopened
and in original packaging.
The model arranged the sentence. Every fact in it — the amount, the window, the condition — came from a document you own, and the source is attached so a wrong answer can be traced rather than argued about.
Document ingestion and chunking strategy, embedding selection, hybrid search combining vector and keyword retrieval, reranking, and citation of source passages so every answer is checkable. Retrieval quality — not model choice — is what determines whether a RAG system is trusted, so this is where most of our engineering effort goes. Deciding what to build first is AI strategy and use-case selection.
Conversational interfaces grounded in your own content, with conversation memory, escalation to humans on low confidence, and refusal behaviour tuned so the system says "I don't know" rather than inventing an answer.
Drafting and summarisation tools, research assistants, internal copilots, code and content generation, and domain-specific applications built around your workflow rather than a chat box.
Supervised fine-tuning, LoRA and QLoRA adapters, and instruction tuning for tone, format or domain vocabulary — with an honest assessment first, because prompting and retrieval solve most problems more cheaply.
Extraction from contracts, invoices, claims and reports; classification and routing; comparison and clause analysis, with structured output validated against a schema.
Generative steps embedded into real processes — triage, drafting, enrichment, summarisation — with human approval gates where the decision matters.
A shared internal layer providing model access, prompt management, cost tracking, guardrails and audit logging across multiple teams, so every department is not integrating its own way.
GenAI capability added to your existing product or systems. Broader integration work — CRM, ERP, legacy systems — is covered on our AI integration services page.
candidates scored on value, feasibility and data readiness. Some are dropped here, which saves more money than any later optimisation.
what content exists, its quality and structure, and whether retrieval can realistically answer the questions you want answered.
golden dataset built first, then a narrow prototype measured against it.
guardrails, caching, cost controls, observability, human escalation paths and access control.
quality tracked continuously in production, with drift detection and regular re-evaluation as content and usage change.
Models: GPT, Claude, Gemini, Llama, Mistral and open-weight models, with routing by task, latency and cost. Frameworks: LangChain, LlamaIndex, or direct SDK integration where framework overhead is not justified. Vector databases: Pinecone, Weaviate, Qdrant, pgvector. Orchestration: Python with FastAPI, Node.js. Evaluation: Ragas, DeepEval, custom golden datasets and LLM-as-judge with human spot checks. Observability: LangSmith, Langfuse, or bespoke tracing. Deployment: AWS Bedrock, Azure OpenAI, GCP Vertex AI, or self-hosted on your infrastructure where data cannot leave.
Retrieval quality beats model choice, almost every time. A weaker model with excellent retrieval outperforms a frontier model retrieving the wrong passages. The engineering that matters: chunking that respects document structure rather than splitting at arbitrary token counts, hybrid search combining semantic and keyword matching, reranking to put the best passages first, and metadata filtering so a query about one region never retrieves another's policy. Teams that skip this and blame the model rebuild twice.
Build the evaluation harness before you build the feature. Without a golden dataset of questions and acceptable answers, you cannot tell whether a prompt change improved or degraded the system — you are relying on the last five outputs someone happened to look at. We build evaluation first, run it in CI on every change, and track quality over time. This is the single clearest signal separating a production team from a demo team.
Hallucination is managed, not eliminated. Grounding in retrieved context, citation of sources so users can verify, structured output validated against a schema, confidence thresholds that trigger escalation, and refusal behaviour tuned deliberately. Any vendor promising a hallucination-free system is either misunderstanding the technology or misrepresenting it.
Cost per query is an architecture decision. Model choice, context length, caching, and routing simple queries to smaller models are what make unit economics work at volume. A system costing twelve cents per query is fine at a thousand queries a month and ruinous at a million. We model this before building, because it frequently changes the design.
Data governance is not optional in enterprise. Where your data goes, whether it can be used for training, retention periods, PII handling and regional residency. Enterprise contracts with major providers address most of this, but it must be verified rather than assumed. The NIST AI Risk Management Framework is a reasonable structure for the governance conversation, and the EU AI Act sets transparency obligations for general-purpose AI systems that apply to deployers as well as providers.
Fixed-scope proof of concept; fixed or phased pricing for production builds; monthly dedicated squads for ongoing AI product work; retainers for evaluation, tuning and monitoring. Teams new to generative AI usually start with a proof of concept.
Indicative cost: a focused GenAI proof of concept typically runs $8,000–$20,000 over four to six weeks. A production RAG system or LLM application generally lands at $25,000–$70,000. Enterprise GenAI platforms serving multiple teams are larger and phased. Ongoing inference, vector database and observability costs are modelled separately and honestly, because they are recurring and frequently underestimated.
The fastest way to know whether generative AI works for your use case is a four-week proof of concept with a real evaluation dataset — not a demo. Tell us the process you want to improve and we will assess feasibility before you commit to a build.
A proof of concept typically runs $8,000–$20,000; a production RAG or LLM application $25,000–$70,000. Recurring inference and infrastructure costs are modelled separately.
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