01
Use-Case Discovery and Strategy (1–2 weeks)
Workshops to choose the use case, define success metrics and baseline, review data sources and confirm security and compliance constraints.
Generative AI Development Services
PerfectionGeeks builds generative AI applications that run in production: AI copilots inside your software, retrieval-augmented generation (RAG) assistants that answer from your documents, AI agents that complete multi-step tasks, document processing pipelines and fine-tuned models for specialist work. We work with Claude, GPT, Gemini, Llama, Mistral and other open-weight models on AWS, Azure or Google Cloud, and we design in evaluation, guardrails and cost controls before launch. A proof of concept starts at about $15,000 and takes 4–8 weeks; most production builds cost $40,000–$150,000.
12+Years Building Software
200+Projects Delivered
4.9/5Clutch Rating (45+ Reviews)
- Adoption is broad but scaling is rare: McKinsey's 2025 survey found 88% of organisations use AI in at least one function, yet only 7% have fully scaled it. The gap is engineering, data and governance.
- Most business value comes from RAG assistants, copilots and agents built on existing foundation models; training a model from scratch is rarely justified.
- Evaluation, guardrails, access control and cost monitoring are what separate a pilot from a production system.
- Typical cost: $15K–$40K for a proof of concept, $40K–$150K for a production application, $150K–$500K+ for enterprise platforms or custom models.
- Start with one measurable use case, prove it in 4–8 weeks, then scale.
Generative AI Services We Deliver
Generative AI Consulting and Use-Case Discovery
We map where generative AI can save time or create revenue, score each idea on value, feasibility and risk, and pick the first use case with a measurable target and a data plan.
RAG and Knowledge Assistants
Assistants that answer from your policies, contracts, manuals, tickets and databases, with citations, permission-aware retrieval and freshness pipelines so answers stay current.
Custom AI Copilots
Copilots inside your CRM, ERP, help desk or SaaS product that draft, summarise, search and suggest next actions, with the user always in control.
AI Agent Development
Agents that complete multi-step work, such as triaging tickets, preparing reports or updating records, using tools and APIs with approval steps for risky actions.
LLM Integration and API Development
Add generative features to existing apps through a secure model gateway with prompt management, caching, routing between models and usage tracking.
Fine-Tuning and Custom Models
Fine-tune open-weight or hosted models for your tone, format or domain vocabulary when prompting and retrieval are not enough, with evaluation against a baseline.
Document Intelligence
Extract, classify and validate data from PDFs, scans, invoices, claims and contracts, with confidence scores and human review queues.
Generative Media
Image, audio and video generation for marketing, product and training content, with brand controls and content provenance.
GenAI Ops and Support
Monitoring, evaluation runs, model upgrades, prompt tuning and cost optimisation after launch.
High-Value Generative AI Use Cases
| Use case | What it does | Typical success metric |
|---|---|---|
| Customer support assistant | Answers questions from your help centre and order data; drafts replies for agents | Resolution rate, handle time |
| Internal knowledge assistant | Finds and summarises answers across policies, wikis and files | Time to answer, search success |
| Sales and CRM copilot | Summarises accounts, drafts emails, updates records | Rep time saved, pipeline hygiene |
| Document processing | Extracts and checks fields from invoices, claims, KYC files and contracts | Straight-through rate, accuracy |
| Contract and compliance review | Flags clauses and gaps against your playbook with citations | Review time, issues caught |
| Code and IT assistants | Explains code, writes tests, triages incidents | Cycle time, incident time to resolve |
| Content generation | Product descriptions, reports, localisation within brand rules | Output per editor, approval rate |
| Analytics assistant | Natural-language questions over your data warehouse | Self-serve query rate |
Choosing Your Generative AI Development Path
Most companies should start with prompting or RAG on a strong hosted model, measure results, then fine-tune or switch to smaller models where cost or latency requires it.
| Approach | When to use it | Typical cost | Time to build |
|---|---|---|---|
| Prompting on a hosted model (API) | General tasks, fast validation | $5K–$25K | 2–6 weeks |
| RAG on your data | Answers must come from your documents and stay current | $25K–$120K | 6–14 weeks |
| Fine-tuned model | Specific format, tone or domain language that prompting can't achieve | $15K–$75K on top of the app | 4–12 weeks |
| AI agent or agentic workflow | Multi-step tasks that use several systems | $25K–$150K | 6–16 weeks |
| Custom model trained from scratch | Rare: unique data at very large scale and a strategic need to own the model | $150K–$500K+ | 4–12 months |
AI Models and Platforms We Work With
We choose models per task based on quality, latency, cost, context length and where your data is allowed to go, and we keep the architecture model-agnostic so you can switch as models improve.
| Category | Options |
|---|---|
| Hosted frontier models | Anthropic Claude, OpenAI GPT, Google Gemini |
| Open-weight models | Llama, Mistral, Qwen, Gemma, DeepSeek |
| Cloud AI platforms | Amazon Bedrock, Azure AI Foundry and Azure OpenAI, Google Vertex AI |
| Orchestration | LangGraph, LangChain, LlamaIndex, agent SDKs, Model Context Protocol (MCP) |
| Vector search | pgvector, Pinecone, Weaviate, OpenSearch, Azure AI Search |
| Evaluation and observability | LangSmith, Langfuse, Arize Phoenix, custom test suites |
| Image, speech and video | Stable Diffusion family, Imagen, Whisper and speech APIs, video generation APIs |
How We Architect Production Generative AI
Data and retrieval layer
Connectors to your document stores, databases and SaaS tools; cleaning, chunking and embedding pipelines; permission-aware retrieval so users only see answers from content they are allowed to access.
Model gateway
One secure entry point for all model calls with routing between models, prompt versioning, caching, rate limits and per-team usage tracking.
Orchestration and tools
Workflows and agents that call your APIs through scoped credentials, with human approval for actions that change data or send messages.
Guardrails
Input checks for prompt injection and off-topic use, output checks for personal data, policy and format, and grounded answers with citations.
Evaluation and observability
Test sets built from real tasks, automated scoring, tracing of every request, and dashboards for quality, latency and cost per task.
User experience
Streaming responses, source citations, feedback buttons, clear handover to a person and accessible interfaces on web and mobile.
Our Generative AI Development Process
02
Data Readiness and Architecture (1–3 weeks)
Audit data quality and access, design the retrieval pipeline, choose models and hosting, and agree the evaluation plan.
03
Proof of Concept (4–8 weeks)
A working assistant or agent on real data, tested against the evaluation set, with a go/no-go decision based on measured results.
04
Production Build
Integrations, guardrails, access control, UI, monitoring and load testing in two-week sprints with demos.
05
Testing, Red-Teaming and Launch
Accuracy and safety testing, prompt-injection attempts, user acceptance testing and a phased rollout to a pilot group.
06
Operate and Improve
Weekly quality reviews, new test cases from real failures, model upgrades and cost optimisation.
Enterprise AI Security, Compliance and Governance
These controls support compliance; your legal and compliance teams confirm the final position for each use case.
| Area | What we put in place |
|---|---|
| Data protection | Encryption in transit and at rest, private networking to model endpoints, no training on your data by model providers under enterprise terms, data residency by region |
| Access control | SSO, role-based access, permission-aware retrieval, scoped tool credentials, audit logs of prompts and actions |
| AI-specific threats | Defences against prompt injection, data leakage and insecure tool use, informed by the OWASP Top 10 for LLM Applications |
| Quality and safety | Evaluation suites, hallucination checks, citation requirements, human review for high-impact outputs |
| Governance frameworks | Alignment with the NIST AI Risk Management Framework and ISO/IEC 42001 practices; model and prompt change control |
| Regulation | Support for GDPR, HIPAA and sector rules; EU AI Act risk classification and transparency duties where your system falls in scope |
Why Enterprises Choose PerfectionGeeks for Generative AI
Model-agnostic engineering
We use the best model for each task and keep you free to switch providers.
Production focus
Evaluation, guardrails, monitoring and cost controls are part of every build, not an add-on.
Full-stack delivery
One team for data pipelines, backend, integrations, web and mobile interfaces and cloud.
Measured outcomes
Every project starts with a baseline and a success metric, and the proof of concept ends with a go/no-go decision based on results.
You own the work
Code, prompts, evaluation sets and infrastructure are handed over to you.
How Much Does Generative AI Development Cost?
These are planning ranges; your estimate depends on final scope.Main cost drivers: number and quality of data sources, integrations with your systems, accuracy and safety requirements, user volume, compliance obligations, custom UI, and whether fine-tuning or self-hosted models are needed.Running costs: model usage billed per token, vector database and hosting, monitoring tools and ongoing tuning. We estimate cost per task during the proof of concept so there are no surprises at scale, and reduce it with caching, smaller models for simple steps and prompt optimisation.
| Package | What is included | Typical cost | Timeline |
|---|---|---|---|
| Proof of Concept | One use case, real data, evaluation set, working prototype, go/no-go report | From $15,000 | 4–8 weeks |
| Custom Development (most popular) | Production app with integrations, guardrails, access control, UI and monitoring | $40,000–$150,000 | 8–16 weeks |
| Enterprise Deployment | Several use cases on a shared platform, model gateway, governance, SSO, multi-region, support SLA | $150,000–$500,000+ | 4–12 months |
Generative AI Development Timeline
A focused production application usually goes live in 3–4 months from kickoff.
| Phase | Duration |
|---|---|
| Discovery and data readiness | 2–5 weeks |
| Proof of concept | 4–8 weeks |
| Production build | 6–12 weeks |
| Pilot and rollout | 2–6 weeks |
Industry-Specific Generative AI Solutions
Healthcare and Life Sciences
Clinical documentation support, prior-authorisation preparation, patient communication and medical knowledge search, with HIPAA-aligned controls and clinicians in the loop.
Financial Services and Banking
KYC document checks, credit memo drafting, policy and regulation search, customer service assistants and fraud-alert summaries with audit trails.
Insurance
Claims intake, document extraction, adjuster summaries and policy question answering.
Legal and Compliance
Contract review against your playbook, clause extraction, due-diligence summaries and regulatory change monitoring, always with citations.
Retail and E-commerce
Product content at scale, shopping assistants, review summarisation and support automation.
Manufacturing and Supply Chain
Maintenance manuals assistant, quality report analysis, supplier document processing and planning copilots.
Education and Training
Tutoring assistants, course content generation and assessment support within academic integrity rules.
Media and Publishing
Research assistants, drafting and localisation tools, archive search and metadata generation with editorial approval.
Engagement Models
Fixed-scope proof of concept
A defined use case, timeline and price with a measured go/no-go decision.
Dedicated AI team
AI engineers, data engineers, backend and front-end developers and a QA engineer working as an extension of your team.
Build, operate and transfer
We build and run the system, then hand over operations to your team with training and documentation.
Related Reading
Related Reading
Ready to Put Generative AI Into Production?
Tell us the process you want to improve. In a free 45-minute session we will review your use case and data, suggest the right approach and model, and outline a proof-of-concept plan with a fixed price.
Client Testimonials
Frequently Asked Questions
What are generative AI development services?
They cover designing, building and running applications that use generative models to create text, code, images or answers: copilots, RAG knowledge assistants, AI agents, document processing and content tools, including data pipelines, integrations, evaluation, security and support.
How much does generative AI development cost?
A proof of concept starts at about $15,000. A production application usually costs $40,000–$150,000, and enterprise platforms or custom models $150,000–$500,000+, plus monthly model and hosting costs.
How long does it take to build a generative AI application?
A proof of concept takes 4–8 weeks and a focused production application typically goes live in 3–4 months. Enterprise platforms take 4–12 months.
Do you work with proprietary models like GPT and Claude, or only open-source models?
Both. We use Claude, GPT and Gemini through their APIs or cloud platforms, and open-weight models such as Llama, Mistral and Qwen when you need self-hosting, lower cost or more control.
What is RAG and why does it matter?
Retrieval-augmented generation finds the most relevant passages in your documents and gives them to the model, so answers are based on your data, stay current and can cite their sources. It is the most common pattern for business assistants.
Should we fine-tune a model or use RAG?
Use RAG when answers must come from changing information. Fine-tune when you need a consistent format, tone or specialised language that prompting cannot achieve. Many systems combine both.
Will our data be used to train public models?
Not when you use enterprise APIs or cloud AI platforms with the right terms, which exclude customer data from training. For the strictest needs we deploy open-weight models in your own cloud account.
How do you prevent hallucinations?
We ground answers in retrieved sources, require citations, let the system say it doesn't know, test against evaluation sets with known answers and route low-confidence or high-impact outputs to a person.
How do you ensure security and compliance?
Encryption, private networking, SSO and role-based access, permission-aware retrieval, audit logs, prompt-injection defences and alignment with frameworks such as the NIST AI RMF, plus GDPR, HIPAA or EU AI Act requirements where they apply.
Can you integrate generative AI into our existing software?
Yes. We add copilots and assistants to CRMs, ERPs, help desks, intranets and SaaS products through APIs, plugins or a secure model gateway.
How do you measure ROI from generative AI?
We set a baseline before the project, such as time per task, resolution rate or error rate, then measure the same metric during the pilot together with cost per task.
Do you provide support after launch?
Yes. We monitor quality and cost, update test sets, upgrade models, tune prompts and add features under a support plan.