Generative AI That Earns
a Place in Your Workflow
Most generative AI pilots stall because they are built as demos, not systems. We design and ship production generative AI — grounded in your own data, wrapped in the access controls your business already relies on, and measured against outcomes rather than novelty.
End-to-End Generative AI Development Services
From the first feasibility question through to the model running quietly in production, we cover the whole lifecycle.
Discovery & Feasibility
We start by testing whether generative AI is genuinely the right tool. You get a clear view of the data you hold, the workflows worth targeting, and an honest assessment of what is achievable — before any budget is committed to build.
Data Preparation
Generative systems are only as trustworthy as the corpus behind them. We consolidate, clean, chunk and index your documents, then build the evaluation sets that let us prove quality objectively rather than by impression.
Model Selection & Tuning
Commercial API, open-weight model, or a fine-tune of your own — we benchmark candidates against your actual tasks and costs, so the choice is driven by measured performance rather than whichever name is loudest this quarter.
Retrieval-Augmented Generation
We build RAG pipelines that ground every answer in your source material and cite where it came from. Users can verify a response in one click, which is usually the difference between a tool people trust and one they quietly abandon.
Application Development
The model is one component. We build the interface, permissions, audit trail, feedback loop and fallback behaviour around it — the unglamorous engineering that turns a capable model into software your team will actually use.
Evaluation & Monitoring
We instrument quality, latency, cost and drift from day one, with automated regression suites so a prompt or model change never silently degrades output that your business has come to depend on.
How We Build Differently
Four engineering commitments that separate a durable system from an impressive demo.
Grounded, Not Guessed
Every generated claim is traceable to a source document. Where the system cannot find support, it says so rather than inventing a confident answer.
Private by Architecture
Your data stays inside your boundary. We deploy in your cloud or VPC, disable third-party training on your prompts, and keep sensitive material out of external logs.
Human in the Loop
For consequential decisions we design review and approval steps in from the beginning, so accountability stays with a person rather than dissolving into a model.
Costed to Scale
We model token economics before launch and engineer caching, routing and smaller models where they suffice, so unit costs fall as usage grows instead of spiralling.
Where Generative AI Pays for Itself
The applications we are asked for most often — and where the return tends to appear fastest.
Knowledge Retrieval
Answer questions across contracts, policies, manuals and tickets in seconds, with citations, instead of sending staff hunting through shared drives.
Intelligent Drafting
Generate first drafts of proposals, reports, summaries and responses in your house style, leaving your specialists to edit and approve rather than start from an empty page.
Support Deflection
Resolve routine enquiries automatically and hand the genuinely complex ones to an agent with the full context already summarised.
Decision Support
Surface the relevant precedent, clause or data point at the moment a decision is being made, rather than after it has been escalated.
Code & Data Assistance
Accelerate internal engineering with assistants that understand your codebase conventions, schemas and internal libraries.
Personalisation at Scale
Tailor communications and recommendations to each customer without multiplying the size of your marketing or service team.
Why Businesses Choose Inperge for Generative AI
Security Is the Starting Point
We treat access control, data residency, retention and audit as design constraints from the first architecture session — not a hardening exercise bolted on before launch.
Shipped, Not Shelved
We scope to a working slice that reaches real users early, then expand from evidence. It is the most reliable way we know to avoid the pilot that never graduates.
You Own the Result
Documented architecture, readable code, no hidden lock-in. Your team can extend and operate what we build, with us on hand rather than in the way.
Our Generative AI Stack
Models
- Claude
- GPT
- Gemini
- Llama
- Mistral
- Open-weight fine-tunes
Frameworks
- Python
- LangChain
- LlamaIndex
- FastAPI
- PyTorch
- Hugging Face
Vector & Data
- Postgres / pgvector
- Pinecone
- Qdrant
- Weaviate
- Redis
- Elasticsearch
Platform
- AWS
- Azure
- Google Cloud
- Docker
- Kubernetes
- Terraform
Generative AI Development — Common Questions
Traditional models classify or predict against a fixed set of outputs — is this transaction fraudulent, will this customer churn. Generative models produce new content: text, code, images, structured summaries. That makes them suited to work that was previously impossible to automate because every output is slightly different, such as drafting, summarising and answering open questions.
Not on our builds. We deploy inside your cloud account or a private environment and use enterprise API terms that contractually exclude training on your inputs. Where the requirement is absolute, we run open-weight models on infrastructure you control so nothing leaves your network.
Retrieval grounding is the main defence: the model answers only from documents we retrieve from your corpus, and every response carries citations a user can check. We add confidence thresholds and explicit refusal behaviour, then measure factual accuracy continuously against a held-out evaluation set.
Scope drives cost far more than model choice. A focused internal assistant over a defined document set is a materially smaller undertaking than a customer-facing platform with multiple integrations and compliance review. We size a project after discovery and quote a fixed price for a defined first phase, so you can commit incrementally.
We aim to have a working, evaluated prototype in front of real users within the first phase rather than at the end of the engagement. Full production rollout depends on integration and review requirements, but you should be assessing genuine output early, not reading status reports.
Yes. Most of the engineering in these projects is integration — CRM, ERP, document stores, ticketing, intranets and data warehouses. We build against your existing authentication and permissions so users only ever see what their role already entitles them to.
Other AI Solutions We Build
Generative AI Consulting
Strategic roadmaps that take AI from boardroom ambition to measurable production workflows.
AI Chatbot
Context-aware conversational interfaces that resolve customer questions around the clock.
AI Automation
Intelligent automation that removes repetitive work so your team can scale without scaling headcount.
AI Agent
Autonomous agents that plan, use tools and complete multi-step work under your supervision.
Ready to move past the pilot stage?
Tell us the workflow you want to improve. We will tell you honestly whether generative AI is the right tool, and what it would take to ship it.