Output Spec & RisksWeek 1
Define formats, brand rules, banned claims, and where a human must approve.
Generative AI Development
Generative AI Apps With Brand Control & Measurable Quality
Generate text, images, code, and structured data inside your product with evals so quality does not drift.
- Eval suites before launch
- Brand and safety rails
- Private or VPC models
4.8×Faster drafts
81%Accepted first pass
BrandStyle locked
Eval suiteQuality tracked
ModerationOn by default
- 60+Projects Delivered
- 7+Years Shipping Software
- 30+Team Members
- 24/7Support Available
GenAIGenerative AI
Overview
GenAI Features Users Accept on the First Pass
We build generative AI into products: writers, copilots, summarizers, document extractors, and media pipelines with style guides and review queues where brand risk is real.
Success is not “it generated something.” Success is acceptance rate, edit distance, time saved, and zero surprise compliance issues.
- LLM product features
- Brand style enforcement
- Document understanding
- Red-team evals
Capabilities
What We Build for Generative AI
Capabilities we deliver under generative ai development scoped to outcomes you can measure.
LLM App Development
Product features powered by LLMs: writers, copilots, summarizers, and assistants with your UX.
- LLM
- Product
- UX
Brand-Safe Content Systems
Tone, style guides, and review queues so marketing output stays on-brand.
- Content
- Brand
- QA
Document Understanding
Extract fields, classify, and summarize contracts, invoices, and tickets at scale.
- OCR
- NLP
- Docs
Code & Developer Copilots
Internal copilots over your repos, runbooks, and APIs to speed engineering work.
- DevTools
- RAG
- Git
Image & Media Pipelines
Generation and editing workflows for catalogs, ads, and creative ops with moderation.
- Vision
- Media
- Moderation
Evaluation & Red Teaming
Automated eval suites, adversarial tests, and dashboards before and after launch.
- Evals
- Safety
- Metrics
Plan your generative ai roadmap
Use Cases
Where Generative AI Fits Best
Concrete workflows where generative ai development pays off fastest.
Marketing content studios
Campaign drafts, product descriptions, and localization with mandatory brand checks.
- Marketing
- Brand
Contract and invoice copilots
Highlight clauses, extract fields, and summarize risk for legal or finance review.
- Legal
- Finance
Catalog and creative generation
Product imagery variants and ad creatives with moderation before publish.
- Retail
- Creative
Engineering knowledge copilots
Answer “how do we deploy X?” from internal repos and runbooks.
- DevTools
- Internal
Examples
Sample Engagements & Outcomes
Illustrative delivery patterns from generative ai work scoped to similar problems.
SKU description generator
Merchandising team cut draft time dramatically; editors only fixed edge cases flagged by style classifiers.
- Retail
- Content
Contract clause assistant
Lawyers got clause maps and redline suggestions grounded in playbooks not free-form legal advice.
- Legal
- RAG
Internal code Q&A
New hires queried architecture docs with citations into the monorepo, reducing senior interrupt load.
- Eng
- Copilot
Approach
Evals Are the Product Spec
Generative features drift. We ship dashboards for acceptance rate and failure clusters so quality stays a KPI, not a vibe.
Book a Discovery CallWe write golden-set tests for tone, factuality, and safety before we argue about which model is “best.”
- Golden-set evals
- Brand/style classifiers
- Human review for high-visibility output
- Cost per accepted draft
Tech Stack
Stack for Generative Products
Generation, retrieval, moderation, and eval harnesses wired into your product UX.
Generation
- LLM APIs
- Self-hosted LLMs
- Image models
- Structured output
Grounding
- RAG
- Style guides
- Template fills
- Tool calls
Safety
- Moderation
- PII filters
- Red teaming
- Review queues
Quality
- Eval harness
- A/B prompts
- Analytics
- Feedback loops
Process
GenAI Delivery Path
Specify quality, then generate not the other way around.
Step 01
Step 02
Eval-Driven PrototypingBuild
Build golden sets, compare models/prompts, and lock the stack on acceptance metrics.
Step 03
Product IntegrationShip
Embed in your UI with review states, versioned prompts, and moderation on by default.
Step 04
Quality OpsOngoing
Monitor drift, refresh golden sets, and expand templates that win acceptance.
Why Nihon Infotech
Generation You Can Publish
We care about first-pass acceptance and brand safety not novelty demos.
Brand rails included
Style guides and classifiers before public output.
Private deploy options
VPC or self-hosted when data cannot leave.
Editor-friendly UX
Diffs, regenerate, and approve flows your team will use.
Honest metrics
Acceptance rate and edit distance, not vanity token counts.
Recent Projects
Look at latest works gallery
E-Commerce Website
Shopify-based eCommerce website for party supplies, built for seamless shopping experiences and scalable online sales.
Visit website
Business Website
WordPress business website for a professional cleaning service, designed to generate customer inquiries and qualified leads.
View project
Personal Brand Website
Personal brand WordPress website created to showcase services, portfolio work, and drive client bookings.
Visit website
Food Delivery Mobile App
React Native food delivery app connecting users with local restaurants, featuring real-time order tracking, secure payments, and an intuitive mobile experience.
View project
RetailSync Inventory System
Custom inventory management web application built to help retailers track stock, manage suppliers, and streamline daily operations.
View project
Service Booking Platform
WordPress-based service booking website designed to showcase offerings, accept customer requests, and generate qualified business leads.
View project
FAQ
Generative AI Questions We Hear Most
Straight answers about generative ai development before you commit budget.
- Fixed-scope proposals
- You own the code
- Direct access to builders
It depends on privacy, cost, and task. We prototype across providers and lock the stack once quality and unit economics are clear.
Prompt constraints, retrieval from approved sources, classifiers for unsafe content, and human review for high-visibility output.
Yes. We deploy self-hosted or VPC-isolated models when data cannot leave your environment.
We define task-level metrics up front acceptance rate, edit distance, time saved and tune until they hold in production.
We start with prompting and RAG. Fine-tuning happens when volume and a stable label set justify the cost.
Yes. We build review UIs with regenerate, version history, and checklist gates before publish.
Let's Build Together
Planning a Generative Feature?
Tell us the output format and brand risks. We will propose evals, a stack, and a first milestone.
Get In Touch
Let's Discuss Your Project Requirements.
Speak with our experts and receive a tailored solution, timeline, and cost estimate.
- Project Inquiriesinfo@nihoninfotech.com
- Talk to an Expert+91-86999-43786
Our Locations
Our Offices
MohaliMohali, Punjab
Plot No. 341, Industrial Area Mohali Phase 9, S.A.S Nagar, Punjab 160062Get Directions
Dubai