Why one LLM isn't enough: routing prompts across models without losing control
Model quality changes every month. Here's how we design routing, fallbacks and cost caps so your product never depends on a single AI vendor.
Copilots, chat assistants and content engines that know your products, policies and voice — built with retrieval, guardrails and evaluation so they're accurate enough to put in front of customers.

Built with the technology trusted by the world's best product teams
Good work shows up on the P&L, not just in the demo. These are the outcomes we design toward on every generative ai & llm apps engagement.
Discuss your projectPick one capability or hand us the whole problem — every service below is delivered by the same senior team that scoped it.
In-app assistants that help users complete tasks, not just chat.
Bots that resolve real queries from your help centre and hand off cleanly to humans.
Brand-safe drafting for articles, product copy and social — with editorial review built in.
Ask-anything search across docs, wikis and tickets with source links.
Phone and WhatsApp voice assistants in English, Hindi and regional languages.
Chunking, embeddings and re-ranking tuned to your content, not a generic demo.
Topic limits, PII filters and automated test suites that catch regressions.
Connect to CRM, helpdesk, CMS, Slack, WhatsApp and your internal APIs.
Real products, real numbers — work we built or ran ourselves.
Startups get speed and focus. Growing companies get structure and scale. Established businesses get modernisation without disruption. Same team, adjusted to where you are.
Six phases, each with a clear output you sign off on — so budget, scope and quality stay visible from day one to launch and beyond.
Workshops to pin down users, outcomes and constraints. You get a scoped backlog, risks and a fixed or ranged estimate.
User flows, wireframes and a clickable prototype tested with real users before a line of production code.
Stack, data model, integrations and security decided up front — documented so any engineer can pick it up.
Two-week sprints with a working demo every sprint. Shared board, shared repo, no black boxes.
Automated and manual testing, performance and security checks, then a staged rollout with monitoring.
Post-launch SLAs, analytics reviews and a roadmap for the next release — we stay until it's working.
We pick proven tools for the job — not whatever is trending — and document every choice so your team can own it later.
We run a content network — so we know what good AI-assisted editorial workflows look like.
Experience with Indian languages and code-mixed text, not just English.
Every answer is grounded and traceable; hallucinations are tested for, not hoped against.
A working pilot in your hands within weeks to prove value before scaling.
Caching, routing and token budgets keep running costs predictable.
Prompts, pipelines and data stay yours.
| Engagement model | Best for |
|---|---|
| Fixed-scope project | MVPs and well-defined builds |
| Dedicated team | Long-term product development |
| Growth retainer | SEO, content and paid acquisition |
| AI sprint | Prototyping an AI feature in 2–4 weeks |
| Staff augmentation | Adding senior engineers to your team |
Behind every review is a business problem solved — a funnel leaking money, a site too slow to rank, a team drowning in manual work. Here's how clients describe working with us. Names are withheld to respect client confidentiality.
Spend went up only when the numbers justified it. Tracking was rebuilt properly before we scaled, and the weekly reviews were blunt and genuinely useful. We grew four times over without losing control of margins.
Read the case studyThe publishing stack and SEO playbook changed how quickly our stories get found. The team understood newsroom pressure, not just code, and breaking news now goes live in minutes.
Read the case studyThey pushed back on features that didn't matter and shipped the ones that did. The routing layer they built saved us from betting everything on a single AI vendor.
Read the case studyOur team used to open dozens of analytics tabs every morning. Now we see every site, live, in one place — and the browser extension is the first thing our editors check.
Read the case studyThe AI publishing engine replaced hours of copy-pasting. Live progress for every job means we always know exactly what went out, and where.
Read the case studyThe website shows our work beautifully on phones, and enquiries come straight to us instead of through marketplaces that take a cut.
Read the case studyQuick answers to what founders and product leaders usually ask us first.
Yes. Retrieval-augmented generation restricts answers to your approved sources and shows citations.
Yes — we build and evaluate for Hindi, Telugu, Tamil and other languages, including mixed-language input.
It depends on volume and model choice. We estimate monthly running costs during scoping and design for them.
Yes. We build escalation paths into your helpdesk or WhatsApp team with full conversation context.
Field notes from building AI products, engineering publishing backends and running growth for our own brands and our clients'.

Model quality changes every month. Here's how we design routing, fallbacks and cost caps so your product never depends on a single AI vendor.

The technical SEO, publishing cadence and internal-linking habits behind a content network with millions of monthly readers.

A practical framework for scaling ad budgets on real numbers — contribution margin, payback period and creative fatigue.
Tell us what you're building. You'll get a reply from a senior engineer — not a sales script — within one business day, with a suggested approach and a ballpark.
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