Claude
by AnthropicAnswers grounded in your documents with citations, structured extraction from PDFs and messy inputs, and copilots that draft, summarise and classify inside your product.
AI and agents
AI product engineering
From prototype to production: RAG, copilots, multimodal features and the evals that keep them honest.
What we deliver
We build AI into products: retrieval-augmented search and assistants over your documents, copilots inside your apps, multimodal and generative-media features, and the evaluation, monitoring and cost controls production needs.
AI features people actually use
Retrieval-augmented generation (RAG) over your documents and data, with citations users can check.
Copilots inside your product that draft, summarise, classify and extract structured data from messy inputs.
Multimodal features that understand images, PDFs and video, and generate on-brand images and short clips.
Engineered for production
We prototype quickly, then harden: structured outputs validated against schemas, prompt caching and model routing to control cost, latency budgets, fallbacks when a provider is unavailable, and a log of every model call.
Quality is measured, not assumed. Evaluation suites built from your real cases run on every change, so you can see whether a new prompt or model is actually better.
Proof we can show you
LuxeLens AI Studio, our own product, renders product shots on Gemini image models and short clips on Veo 3.1, grounds each product with Google Search before generating, and keeps uploads in private Google Cloud Storage behind expiring links.
We built Vyakarni, a Hindi-first AI writing assistant, for SNS Innovation Labs, and we are developing Infra-Q, an infrastructure-intelligence product for Greenberg Engineering that is now in internal beta.
Typical deliverables
- Use-case validation and a working prototype
- RAG pipelines with citations
- Structured extraction and classification
- Multimodal and generative-media pipelines
- Evaluation harness and quality reports
- Cost, latency and usage monitoring
- Production deployment and handover
Tools we typically use
- Claude (Opus, Sonnet, Haiku)
- Gemini (Argon, Flash, Flash-Lite)
- Gemini image models and Veo
- Embeddings with pgvector or managed search
- Gemini Enterprise Agent Platform
- Next.js
- Postgres and Supabase
- Google Cloud Storage
If your team already runs a different stack, we work within it. These are defaults, not requirements.
Claude and Gemini
Where Claude and Gemini fit
We build on both model families and choose per task, on evaluation results, cost and latency, inside the cloud you already use.
Gemini
by GoogleMultimodal understanding of images and video, and generative media: on-brand images with Gemini image models and short clips with Veo, as in LuxeLens AI Studio.
Together. We choose per feature on evaluation results, cost and latency, and route simple work to small, fast models.
Phases and team
How an engagement typically runs
6–16 weeks, delivered by a small senior pod, with progress you can see as it is built. Pip tailors the working model to your team.
Validate
Confirm the use case, the data it needs and how quality will be measured.
Prototype
A working feature on real data, so you can judge it before committing to the full build.
Harden
Structured outputs, evals, cost and latency budgets, fallbacks and monitoring.
Launch and manage
Production release, handover and ongoing tuning as models and usage change.
Typical team
Solution architect, AI engineers, full-stack engineer and QA/evals engineer
Pricing
Priced to your scope and market.
Every project is priced to its scope and market. Pip gives you a tailored range in minutes, and the final number comes in a written proposal.
Hosting, subscriptions, licences and AI usage are billed to your own accounts at cost, estimated up front.
Related work
Where we've done this
LiveOur generative product-photo and video studio · Gemini on Google Cloud
- Generative AI
- Gemini
- Google Cloud
In developmentAI infrastructure-intelligence product · In internal beta
- AI product
- In development
- Construction tech
LiveAI Hindi grammar and writing assistant · Built end to end for SNS Innovation Labs
- AI product
- Indian languages
- Education
FAQ
AI product engineering: common questions
Ask Pip if yours isn't here.
Rarely at the start. Most products get further with good retrieval, clear prompts, structured outputs and evaluation. We recommend fine-tuning only when evals show a gap that those can't close.
We route simple tasks to small, fast models, cache stable prompts, batch offline work, and set budgets and alerts. Model usage is billed to your own provider account at cost, so you always see it.
Yes: Postgres, BigQuery, Google Drive, SharePoint and similar sources, with read-only access by default and nothing copied that doesn't need to be.
Further reading
Articles and playbooks on AI product engineering
Playbooks are reference architectures we propose for an industry, not past client results.
- Article · 4 min readProduction LLM pipelines: structured outputs, tool use, evals and guardrailsModel output is non-deterministic. The engineering patterns we use to turn Claude and Gemini into dependable parts of real business workflows.
- Article · 3 min readBuilding AI products for Indian languages: what changes beyond EnglishHindi and other Indian languages raise practical issues English-first teams miss: Unicode normalisation, token cost, code-mixing, evaluation and voice. A field guide.
- Playbook · Retail and e-commerceCatalogue and content factoryBatch-generate product descriptions, attributes, alt text, translations and on-brand imagery for thousands of SKUs with Claude and Gemini, with a human review queue.
- Playbook · Healthcare and life sciencesClinical and regulatory document draftingDraft protocols, SOPs, discharge summaries and submission sections from approved sources with Claude, with citations, template checks and expert sign-off.
- Playbook · EducationAI tutor with text and voiceA Socratic tutor grounded in your course material, with a voice mode in many languages, audio lessons and rubric-based grading that teachers review.
- Playbook · Manufacturing and B2BShop-floor SOP and maintenance copilotGive technicians cited answers from manuals and SOPs, hands-free voice Q&A on the floor and video review of procedures, with Claude and Gemini.
Related services
All services
AI agents and automation
Agents that answer, act and hand off, on Claude and Gemini, inside the tools you already run.
Case studies: LuxeLens AI Studio
- Typical timeline
- 4–10 weeks
Custom web apps and platforms
Fast, accessible web products, from MVP to platform, engineered to last.
Case studies: Greenberg Engineering, Vyakarni (व्याकरणी), Infra-Q
- Typical timeline
- 6–18 weeks
Data, analytics and automation
Turn scattered data and repetitive work into dashboards, pipelines and reviewed automations.
- Typical timeline
- 3–12 weeks
Pip · Softpiper's AI
Ask Pip
Get a tailored range in a couple of minutes
Pip is Softpiper's AI. Describe the project and it will ask a few questions, give you a tailored price range for your scope and market, and book a call with the team.