Generative AI Development
Generative AI applications for text, documents, code and media — grounded in your data and engineered for production reliability.
- Understand requestIntent: refund · order #4821
- Retrieve policyRefund policy v3 · 2 sources cited
- Call toolsorders.lookup · payments.status
- 4Human approvalRefund above auto-approve limit
- 5Execute actionpayments.refund
- 6Respond & logCustomer reply · audit trail
Tool call
orders.lookup({
order_id: "4821"
}) → { status: "delivered",
amount: 42.00 }Approval required
Refund 42.00 to original payment method
Division
Service area
Generative AI & LLM Applications
Engagement
Project · Team · Managed
Overview
Generative AI can draft, summarise, translate, extract, classify and converse, but production value depends on control. We build generative AI features and applications with retrieval grounding, structured outputs, guardrails and evaluation so results are consistent enough to put in front of customers and regulators. Typical work ranges from content generation pipelines and document summarisation to multimodal applications combining text, images and audio.
Common use cases
- Content generation at scaleProduct descriptions, reports and communications generated from structured data with brand and compliance rules.
- SummarisationLong documents, calls and case histories condensed into consistent, reviewable summaries.
- Structured extractionUnstructured text turned into validated JSON for downstream systems.
- Multimodal analysisImages, PDFs and screenshots interpreted alongside text.
Quick answers
Generative AI Development at a glance
The essentials in brief. Every project is scoped individually — ask us for specifics.
- What is generative AI development?
- Generative AI applications for text, documents, code and media — grounded in your data and engineered for production reliability.
- Who is it for?
- Typically product companies adding AI features, enterprises automating knowledge work, and teams whose AI pilot needs to become a dependable production system.
- What does Shivacha provide?
- Prompt & output engineering
- Retrieval grounding
- Guardrails
- Human review workflows
- Model routing
- Evaluation harness
- Which technologies are used?
- Large Language Models, OpenAI Models, Retrieval-Augmented Generation, Embeddings, Vector Databases, Fine-Tuning — chosen to fit your stack and constraints.
- How does the process work?
- Define the job → Build the evaluation set → Prototype retrieval and prompts → Harden for production → Roll out and learn.
- What affects the cost?
- Number and quality of data sources
- Accuracy targets and evaluation effort
- Integrations with business systems
- Model choice, hosting and per-request cost
- Human-in-the-loop and audit requirements
- Data residency and privacy constraints
- How long does it take?
- A scoped proof of value usually takes 4–8 weeks; a production system with evaluation, integrations and guardrails typically takes 3–6 months.
- How do I get started?
- Share a short brief in the form below, book a 30-minute call or message us on WhatsApp. A senior engineer replies within one business day; NDA on request.
Capabilities
What we deliver
Prompt & output engineering
Versioned prompts with schema-constrained outputs and validation.
Retrieval grounding
RAG pipelines that tie generation to approved sources.
Guardrails
Input and output filtering for safety, PII and policy compliance.
Human review workflows
Approval queues for high-stakes generated content.
Model routing
Task-appropriate model selection to balance quality, latency and cost.
Evaluation harness
Automated scoring of accuracy, tone and format on every change.
Architecture
Engineered right from day one
The layers we typically design for generative AI & LLM applications, adapted to your stack and partners.
- Data boundariesDocument-level permissions are enforced at retrieval time so users only see answers from content they may access.
- Grounding & citationsAnswers link to their sources so users can verify, and out-of-scope questions are declined.
- Model portabilityAn abstraction layer lets you swap or mix model providers without rewriting the application.
- Unit economicsSemantic caching, prompt compression and model routing keep per-request cost predictable.
Delivery
How an engagement runs
- 1
Define the job
Which questions or tasks, for which users, with which data — and what a correct answer looks like.
- 2
Build the evaluation set
Representative questions with expected answers, used to score every iteration.
- 3
Prototype retrieval and prompts
A working slice against real documents, measured against the evaluation set.
- 4
Harden for production
Access control, guardrails, fallbacks, caching, streaming and observability.
- 5
Roll out and learn
Staged rollout, feedback capture and scheduled re-evaluation as content and models change.
Security
Security built into delivery
Controls we apply by default on this kind of work — not a separate phase at the end.
Data boundaries
Permission-aware retrieval so users only see answers from documents they may access.
Guardrails
Input and output checks, tool allow-lists and human approval for consequential actions.
No training on your data
Provider settings and contracts chosen so your data is not used to train third-party models.
Audit trail
Prompts, sources, tool calls and approvals logged for review.
Technology
Tools we use for this
Products
Start from a platform
Shivacha AI Support
AI support that resolves, not just responds — grounded in your knowledge and systems.
Learn moreShivacha Copilot Kit
Embed a context-aware AI copilot into your product or internal tools.
Learn moreShivacha AI SaaS Starter
The foundations of an AI SaaS product — ready to build your differentiation on.
Learn moreRelated services
Often combined with
LLM Development
LLM application and platform engineering: model selection, fine-tuning, serving, evaluation and cost-optimised inference.
Learn moreRAG Development
Retrieval-augmented generation systems that ground LLM answers in your documents with permissions, citations and measurable accuracy.
Learn moreAI Chatbot Development
LLM-powered chatbots for customer service, sales and internal support — grounded, on-brand and integrated with your systems.
Learn moreDedicated team
AI Engineering Team
AI engineers who build production LLM applications, RAG systems and AI features.
Work & insights
Related thinking
Permission-aware enterprise knowledge assistant
How we design a RAG assistant that answers from thousands of internal documents while respecting every user's access rights.
Learn moreMulti-tenant SaaS foundation built for enterprise readiness
The foundations we put in place when building a SaaS product that will need to sell to enterprises.
Learn moreAI development cost: what you pay for when you build an AI product or agent
Model fees are rarely the main cost. Data preparation, evaluation, integrations and guardrails decide both the budget and whether the system works.
Learn moreFAQ
Frequently asked questions
Is generated content accurate enough for customers?
With grounding, validation and, where needed, human review, generative AI can meet customer-facing standards. We measure accuracy on your data before launch rather than assuming it.
Who owns generated outputs?
Outputs belong to you under your agreements with model providers; we select providers and configurations that align with your data and IP requirements.
Should we fine-tune or use retrieval?
For most knowledge-grounded use cases, retrieval-augmented generation is the right starting point: it is cheaper, keeps knowledge current and supports citations. Fine-tuning helps with style, format, domain vocabulary or narrow classification tasks, and is often combined with retrieval.
How do you measure LLM quality?
With task-specific evaluation sets scored for correctness, groundedness, completeness and format, using a mix of automated checks, model-graded evaluation and human review for high-stakes outputs.
Next step
Build Your AI Product.
Tell us about your generative AI development requirements — goals, timeline and constraints. We will reply with questions, an approach and next steps.
- Senior engineer reads every enquiry
- Reply within one business day
- NDA on request
Your details are used only to reply to this enquiry.