Stage 11 to 2 weeks
AI Opportunity Sprint
Teams who know AI matters but not where to start.
A short, structured discovery that finds the AI opportunities worth pursuing in your business, and the ones that aren't.
Find your AI opportunityAI product development
Applied AI engineering for the products you already run
We add AI features to web, mobile, SaaS and ERP products the way senior product engineers would: designed around a user job, built on your stack, tested against real data and shipped with monitoring.
The same team builds the AI, the product interface and the backend, so there are no hand-offs between vendors.
What usually goes wrong
Adding AI to a product is easy to demo and hard to ship. The gap between a promising prototype and a feature customers trust is mostly engineering: data access, latency, cost, evaluation and user experience.
Prototypes that never reach production
A notebook or hackathon demo works on ten examples. Nobody has tested it on thousands, planned for failures or estimated what it costs at scale.
AI bolted on instead of designed in
A generic chat box in the corner rarely helps. Users want AI inside the workflow they already use: pre-filled fields, better search, smarter defaults.
Unpredictable cost and latency
Model calls add seconds and variable spend to every request. Without caching, routing and limits, a popular feature can become an expensive one.
No way to tell if it is getting better or worse
Without evaluation data, every prompt change or model upgrade is a guess, and regressions reach customers first.
Where Applied AI Engineering earns its keep
Intelligent and semantic search
Let users search by meaning ("trucks available near Pune tomorrow") across products, records and documents, with filters that still behave predictably.
Recommendations
Suggest next products, content, courses or actions based on behaviour and context, with business rules that keep suggestions relevant and safe.
Summarisation
Condense long threads, reports, case histories or meeting notes into summaries that appear where the user needs them.
Classification and tagging
Automatically categorise tickets, listings, transactions or content so downstream features and reports work without manual tagging.
Data extraction in-app
Turn uploaded files, photos and pasted text into structured fields, so users confirm information instead of typing it.
Personalised product experiences
Adapt onboarding, dashboards and messaging to each user's role and history, within guardrails you control.
What we actually build
Feature design grounded in a user job
Product and UX design that decides where AI helps, what the user sees when it is wrong, and how they correct it.
Model integration layer
A clean service that wraps model providers with structured outputs, retries, caching, timeouts and fallbacks, so you can switch models later.
Retrieval and data pipelines
Embeddings, vector search and data sync from your database, so the feature works on your content with correct permissions.
Web and mobile interfaces
Next.js, React, React Native and Flutter front ends with streaming responses, loading states and clear user controls.
Evaluation harness
A test set drawn from real usage, automated scoring and a regression check that runs before every release.
Usage, cost and quality monitoring
Per-feature dashboards for latency, spend, errors and user feedback, so product decisions are based on data.
Systems we connect to
The usual suspects. If yours has an API or a database, we can almost certainly work with it.
- Web & mobile
- Next.js, React, React Native, Flutter
- Backends
- Node.js, Python / FastAPI, .NET, Serverless functions
- Data
- PostgreSQL + pgvector, MongoDB, Firebase, Vector databases
- Model providers
- Anthropic Claude, OpenAI, Open-weight models, Cloud AI platforms
How it works, step by step
- 01
User action
A user searches, uploads, types or opens a screen where the AI feature is available.
- 02
Context assembly
The backend gathers only the data this user is allowed to see (records, history, documents) and prepares a compact prompt.
- 03
Model call with guardrails
The request goes through the integration layer, which enforces structured output, timeouts, cost limits and content rules.
- 04
Validated result in the UI
Results are checked, then shown in the product with sources, confidence cues and an easy way for the user to edit or reject them.
- 05
Feedback loop
User corrections and ratings are captured and feed the evaluation set that protects the next release.
Blueprint: semantic search inside a SaaS product
Reference design, not a client projectThe scenario: A multi-tenant SaaS platform wants users to find records by describing them in plain language, without leaking data between tenants.
- 1. TriggerUser queryPlain-language search in the app
- 2. Rules & checksTenant filterPermission scope applied before retrieval
- 3. AI stepHybrid retrievalKeyword + vector search, reranked
- 4. System updateAnswer with sourcesResults and a short summary with links
- 5. Human controlUser feedbackClick-through and corrections logged
- Trigger
- Rules & checks
- AI step
- System update
- Human control
How we deliver it
Timings are typical for a first release. They depend on scope, how ready your data is and the integrations involved, so we confirm them after discovery.
- 1Week 1
Feature framing
We define the user job, success metrics, data sources and acceptable failure modes, and check feasibility on real samples.
- 2Weeks 2 to 4
Thin working slice
We build one end-to-end path through your real stack, covering interface, backend, model layer and evaluation, behind a feature flag.
- 3Weeks 4 to 8
Hardening
We expand the test set, tune quality, cost and latency, add monitoring and run a limited beta with real users.
- 4Weeks 6 to 12
Launch & iterate
We roll out gradually, watch the dashboards and ship improvements based on usage and feedback.
Safeguards, built in from day one
Security, privacy, testing and human control are designed in from the start, not bolted on after a pilot. We adapt them to your policies and your risk.
Permission-aware context
The model only receives data the current user is already allowed to access. Tenant and role filters are applied in code, not in the prompt.
Data handling choices
We document which data leaves your infrastructure, prefer provider settings that exclude your data from training, and can redact personal data before model calls.
Regression evaluations
A versioned test set scores every prompt, model or retrieval change before release, so quality does not quietly drift.
User stays in control
AI output is presented as a suggestion the user can edit, accept or reject. It never silently changes their data.
What you can expect
We agree product metrics before building and track them after launch. Depending on the feature, goals usually include:
- Users finding what they need in fewer steps
- Less manual data entry inside your product
- Higher adoption of features that were previously hard to use
- Predictable per-request cost and latency
- A differentiated product experience in your market
What we measure
Task completion and time on task, Feature adoption and retention, Evaluation score per release, Cost and latency per request.
We don't promise savings or accuracy figures up front. We measure them on your data during the pilot.
Where it fits best
- SaaS & digital products. Semantic search, in-app assistants and smart onboarding.
- Education. Course and content recommendations, report-card summaries.
- Logistics. Natural-language shipment search and delay summaries.
- ERP & operations. Auto-categorised transactions and narrative reports.
Ways to begin
Stage 23 to 5 weeks
Workflow Automation Pilot
One valuable workflow you want to prove before scaling.
A focused pilot that automates a single workflow end to end, running against a measured baseline so the result is a decision, not a demo.
Start an AI pilotStage 36 to 12 weeks
Production AI Build
An AI product or feature you are ready to ship to real users.
A full build from product design to deployment: the AI, the application around it, the integrations and the tooling to run it.
Plan a production buildStage 4Ongoing, monthly
Managed AI Operations
AI systems already in production that need an owner.
Ongoing care for production AI: we watch quality, control costs, handle model changes and keep security reviews current.
Talk about managed AI
Applied AI Engineering: questions we get asked
What is applied AI engineering?
Applied AI engineering is the work of turning AI models into dependable product features: designing the user experience, connecting the right data, controlling cost and latency, evaluating quality and running the feature in production.
Can you add AI to an existing app you did not build?
Yes. We start with a short technical review of your codebase and data, then add the AI feature as a well-bounded service your team can own afterwards.
Which AI models do you use?
We choose per feature based on quality, cost, latency and data-handling needs. That can mean commercial models such as Claude or GPT, or open-weight models when data must stay in your environment. The integration layer keeps you free to switch.
How do you keep AI features affordable at scale?
Through caching, smaller models for simple steps, prompt and context trimming, rate limits and per-feature cost dashboards. We estimate cost per request before launch.
How long does it take to ship an AI feature?
A focused feature typically goes from framing to a gated beta in four to eight weeks, depending on data readiness and integration complexity. We'll give a specific estimate after the framing week.
Planning an AI feature for your product?
Share the user problem and your stack. We'll outline a thin working slice and what it would take to ship it properly. We reply within one business day.
We reply within one business day, and we're happy to sign an NDA first. Prefer email? Write to info@originsphere.in.