SofStack is a generative AI MVP development company that builds LLM-powered products for enterprises and established businesses. We turn generative AI ideas into secure, measurable MVPs connected to your own data.
Calling an LLM API is easy. Building a generative AI product that gives accurate answers, protects sensitive data and controls cost at scale is not. That is the work we do.
Generative AI MVPs We Build
RAG Knowledge Assistants
Assistants that answer questions from your policies, manuals, contracts and wikis, with citations to the source documents.
Enterprise Copilots
Copilots embedded in your CRM, helpdesk or internal tools that draft responses, summarise records and suggest next steps.
Document Generation
Tools that produce reports, proposals, product descriptions and compliance documents from templates and structured data.
Conversational AI for Customers
Chat and voice assistants for support, onboarding and sales, with escalation to human agents.
Content and Marketing Tools
Brand-controlled content generation for marketing, localisation and product catalogues at scale.
What Goes Into a Production-Ready GenAI MVP
Component | What it does |
Data ingestion | Connects and cleans documents from SharePoint, Google Drive, Confluence, databases and file stores |
Retrieval layer | Chunking, embeddings, vector search and re-ranking to find the right context for each question |
Model layer | Selects the right LLM for quality, speed and cost, with fallbacks between providers |
Prompt and orchestration | Versioned prompts, structured outputs and multi-step workflows |
Guardrails | Input and output filters, PII masking and topic restrictions |
Evaluation | Test sets and automated scoring for accuracy, relevance and hallucination |
Monitoring | Usage, latency, cost per request and user feedback dashboards |
Model-Agnostic by Design
We choose models based on your requirements, not vendor preference, and design the MVP so you can switch models later.
Commercial LLMs: OpenAI GPT, Anthropic Claude and Google Gemini through enterprise endpoints
Cloud AI platforms: Azure OpenAI, Amazon Bedrock and Google Vertex AI
Open-source models: Llama, Mistral and others, self-hosted when data must stay in your environment
Fine-tuning when prompting and retrieval alone don't reach the quality target
How We Control Accuracy and Hallucinations
Grounded answers: responses are based on retrieved sources and include citations
Evaluation sets: real questions from your teams with expected answers, tested on every release
Confidence handling: the assistant says when it doesn't know instead of guessing
Human review for high-risk outputs such as legal, financial or medical content
Feedback loops: user ratings feed back into prompt and retrieval improvements
Keeping GenAI Running Costs Under Control
LLM costs can grow quickly once usage scales. We design for cost from the MVP stage.
Model routing: smaller, cheaper models for simple tasks and larger models only where needed
Caching of repeated questions and embeddings
Prompt optimisation to reduce token usage without losing quality
Cost dashboards showing spend per user, team and feature
Enterprise Data Security for Generative AI
No training on your data: enterprise model endpoints with zero data retention where available
Permission-aware retrieval: users only get answers from documents they are allowed to see
PII masking before data reaches external models
Deployment in your cloud with audit logs of prompts and responses
NDA and full IP transfer of code, prompts and configurations
Generative AI MVP Pricing
Engagement | Starting price | Timeline |
GenAI Discovery | From $2,500 | 2 weeks |
GenAI Proof of Concept | From $5,000 | 3–4 weeks |
Generative AI MVP | From $15,000 | 8–12 weeks |
Dedicated GenAI Team | From $6,000/month | Monthly |
LLM provider and cloud hosting costs are billed separately to your own accounts.
Frequently Asked Questions
What is a generative AI MVP?
A generative AI MVP is the first usable version of a product built on large language models, such as a knowledge assistant, copilot or content tool. It is tested with real users before a full rollout.
Which LLM is best for our use case?
It depends on accuracy needs, data sensitivity, speed and budget. During discovery, we test several models on your real tasks and recommend the best fit.
Will our data be used to train public models?
No. We use enterprise endpoints that do not train on your data, or self-hosted open-source models inside your cloud.
How do you stop the AI from making things up?
We ground answers in retrieved sources, show citations, test against evaluation sets and configure the assistant to say when it doesn't know.
How long does a generative AI MVP take?
Most generative AI MVPs take 8 to 12 weeks. A proof of concept on your own documents can be ready in 3 to 4 weeks.
What will it cost to run after launch?
Running costs depend on usage and model choice. We estimate monthly LLM and hosting costs during discovery and build cost controls into the MVP.
Build Your Generative AI MVP
Share your use case and the data it needs. We will recommend an approach, a model strategy and a starting engagement.