Generative AI Development Services for Everyday Business Work
Generative AI writes, summarises, answers and creates: replies to customers, first drafts of reports, answers from a policy library, code suggestions, product images. We build these tools for businesses, connected to your documents and systems, with checks that keep the output accurate enough to rely on and a person in the loop where it matters.
built for
- Customer support and service teams
- Marketing and content teams
- Legal, finance and operations teams handling documents
- SaaS companies adding AI features
- Organisations with large document libraries
Draft a product description: A light, waterproof jacket with taped seams and a packable hood, in your brand voice. (illustrative demo response)
key facts
- typical project length
- 4–16 weeks
- models, chosen per task
- Hosted or open-source
- or private cloud
- Azure, AWS, GCP
- of real questions for every build
- Test set
What we deliver
What you get with Generative AI Development. Nine parts of the service, each scoped with you.
We work with hosted models from OpenAI, Anthropic, Google and Mistral, and open-source models such as Llama that you can host yourself. For most business tools, retrieval-augmented generation (RAG) is the core: the system finds the relevant passages in your content and the model answers from them, citing where each answer came from.
Generative models can be wrong with confidence, so evaluation is part of the build, not an afterthought. Each project gets a test set of real questions with approved answers, guardrails on what the system may say and do, and logging so you can see what it answered and why.
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Use-case discovery
We map the tasks where drafting, summarising or searching takes your staff the most time, and pick the first one to build with a clear measure.
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RAG knowledge assistants
Assistants that answer from your manuals, policies and tickets, cite the source passage and say so when the answer is not in your content.
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AI copilots for staff
Writing, research and coding helpers placed inside the tools your team already uses, from the CRM to the code editor.
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Intelligent document processing
Extract, classify and check data from invoices, contracts and forms, with low-confidence fields sent to a person to confirm.
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Content generation
Product descriptions, marketing copy, emails and social posts in your brand voice, reviewed and approved before anything is published.
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Image, voice and speech
Image generation for product visuals and creative drafts, plus speech recognition, voice bots and text-to-speech.
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Chatbots and AI agents
Customer-facing chatbots on web and messaging channels, and agents built with LangGraph, CrewAI or Microsoft Agent Framework that complete multi-step tasks.
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Model API integration
OpenAI, Gemini, Claude, Llama and Mistral connected to your CRM, ERP, CMS or SaaS product through Python and FastAPI services.
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Fine-tuning and governance
Fine-tuning where prompting and RAG fall short, plus usage policies, content filters and audit logs for privacy and compliance.
Why it matters
What changes once it is live. Each outcome is measured, not promised.
Generative AI Development is planned around these outcomes, measured against a baseline we agree with you up front.
Discuss your project- 01
Time back from routine writing
First drafts, summaries and replies arrive in seconds for a person to edit, instead of being written from a blank page.
- 02
Answers people can check
Source citations let staff and customers see where an answer came from, and spot when the model has strayed beyond your content.
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Freedom to change models
We design around a model layer you can switch, so you are not tied to one provider's pricing, limits or roadmap.
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Knowledge that is easy to find
Years of documents become searchable in plain language, so new staff find answers without interrupting colleagues.
How we work
How we deliver Generative AI Development
A clear five-step process, so you always know what happens next and what you will get.
Every project starts with a free, no-obligation consultation.
$ appsinai run
Step 1: Requirements
We agree the task, the users, the data sources, the constraints and the questions the system must answer well.
Step 2: Approach and model choice
We choose between prompting, RAG, fine-tuning or an agent, and shortlist models by output quality, cost, speed and where they can be hosted.
Step 3: Design and build
Architecture, retrieval pipeline, vector database such as Pinecone, Weaviate or Chroma, prompts and user interface are built in short iterations.
Step 4: Evaluate
The system is scored against the test set, checked for invented facts and unsafe answers, and tuned until it meets the agreed standard.
Step 5: Deploy and support
We release to a pilot group, then more widely, monitoring answer quality, cost per request and user feedback, and updating as models change.
↺ results feed the next improvement cycle$
Security & control
Generative AI your security team can sign off
The main questions are what the model sees, where it runs and what it is allowed to say. We answer them in the design, not after launch.
Ask us about security- 01
Your prompts stay out of training
We sign an NDA before you share any data, and use enterprise API terms or settings that keep your prompts and documents out of the provider's training data, or a self-hosted open-source model.
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Permission-aware retrieval
The assistant retrieves only documents the signed-in user is allowed to open, so HR or finance files are not shown to the wrong person.
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Guardrails on input and output
Checks for prompt injection, personal data and off-topic or harmful requests, and rules on which actions need a person's approval.
- 04
Region and hosting choice
Deploy on Azure, AWS or Google Cloud in your chosen region, or on a private cloud, with logs kept in your account.
FAQ
Questions about Generative AI Development
Straight answers to what buyers ask us most. Can't find what you need?
Email sales@appsin.ai or call +91-9025555584.
Ask us directlyWhat does a generative AI project cost?
The main drivers are the number and quality of data sources, whether RAG is enough or fine-tuning is needed, the integrations, the volume of requests (hosted models charge per token) and the hosting choice. A focused assistant over one document library costs far less than an agent working across several systems. We quote a fixed price for the agreed scope.
Estimate your project costHow long does a generative AI project take?
Typically 4 to 16 weeks, depending on scope, integrations and how much customisation is needed. A pilot on one use case sits at the shorter end; a production system connected to several business systems at the longer end.
Which models do you work with?
OpenAI GPT models, Google Gemini, Anthropic Claude, Mistral and open-source models such as Llama. We pick per task, comparing answer quality on your own test questions, cost per request, speed and whether the model can be hosted where your data rules require.
Should we use RAG or fine-tuning?
For answering from your documents, start with RAG: it keeps answers current, shows sources and needs no training. Fine-tuning helps when you need a fixed format, tone or specialised behaviour that prompting cannot reliably produce. Many systems use both.
RAG vs fine-tuning comparedHow do you stop the AI making things up?
No method removes the risk entirely, so we reduce and contain it. Answers are grounded in retrieved passages with citations, the model is told to say when it does not know, outputs are scored against a test set before launch, and high-stakes actions need a person to approve them.
Can it run without sending our data to an outside AI provider?
Yes. Open-source models such as Llama and Mistral can be fine-tuned and hosted on your private cloud or servers, so prompts and documents stay inside your environment. The trade-off is that you carry the hosting cost and may need a larger model for complex tasks.
Can you add generative AI to our existing software?
Yes. We add it to web and mobile apps, CRM, ERP, SaaS products and internal tools through APIs, so users get drafts, answers and summaries in the screens they already work in.
Free PDF brochure
Generative AI Development brochure
Features, outcomes, delivery process and FAQs in one PDF you can share with your team or management.
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related
Keep exploring
- RAG vs fine-tuningHow retrieval and fine-tuning differ on cost, freshness, accuracy and effort, and when to combine them.
- AI Agent DevelopmentAgents that plan and complete multi-step tasks across your systems, with human approval where it counts.
- AI project cost estimatorA quick estimate of what your generative AI project could cost, based on scope and integrations.
- AI & ML DevelopmentEnd-to-end AI and machine learning development, from a feasibility check and proof of concept to models running inside your ERP, CRM or apps.
- AI Chatbot DevelopmentCustomer-facing AI chatbots on your website, WhatsApp and app that answer from your content, complete simple tasks and hand difficult conversations to your team.
- ChatGPT & OpenAI IntegrationOpenAI models built into your apps, SaaS products and internal tools, with retrieval over your data, tested prompts and running costs you can plan for.
- LLM DevelopmentWe select, adapt and deploy large language models for your tasks, from fine-tuned open-source models on your servers to integrations with hosted APIs.
- Model Training & Fine-TuningWe prepare your data and fine-tune or train AI models so they follow your terminology, formats and rules, with evaluation before and after every change.
- Machine Learning DevelopmentCustom machine learning models for forecasting, risk, recommendations and inspection, trained on your own data and evaluated before they reach production.
Free consultation
Pick one task to hand to generative AI
Book a free 30-minute call. Bring the task your team repeats most, and we will outline how a generative AI tool would handle it, which model we would test first and how we would measure it.