How is this different from just calling the API?

Calling the API is the easy 10%. Production AI features need streaming UX, fallbacks, eval suites, prompt-injection defence, schema-validated outputs, caching, routing across providers and per-feature cost dashboards. We build all of that — not just the prompt.

Which models do you use?

Whatever fits the feature. Anthropic Claude and OpenAI for most reasoning, open-weights for cost-sensitive work, smaller fine-tuned models where they outperform frontier ones. Smart routing chooses per-call with fallback to a second provider if the first throttles or fails.

How do you control costs?

Prompt caching, response caching, smaller models on simpler calls, per-feature budgets with paging when they trip, and dashboards that show $$ per feature, per user and per model. We catch runaway spend before invoices do.

How do you handle prompt injection?

Input sanitisation, system-prompt isolation, JSON-schema validation on every model output, and an adversarial eval suite that runs in CI on every prompt change. New attacks get added to the suite — once they pass once, they can't regress.

AI Product Integration Services

AI product integration

Get a free quote See our work

Streaming-first UXPrompt-injection defendedCost guardrails by defaultYou own the code

Next.js

Hand-coded features

Next.js

Hand-coded features

5.0

Google rating

Specialist services

24h

Reply within

Feature patterns

Drafting01

In-product drafting & rewriting

Generate, rewrite, expand or shorten — inline in your editor, with selection-aware context and one-click accept.

Selection-aware prompts
Streaming token UI
Undo + diff view

Summarise02

Smart summarisation

Meeting recaps, long-doc condensing, daily digests — with adjustable length and styles your team can switch between.

TL;DR + bullets + briefs
Source-grounded recaps
Scheduled digests

Classify03

Classification & routing

Auto-tag, prioritise and route tickets, leads or content with schema-validated outputs and confidence scores.

Multi-label + hierarchical
Confidence-aware routing
Human review fallback

Copilot04

In-product copilots

Side-panel assistants that know what the user is doing in the app — and act on that context without context-switching.

Context-aware prompts
Action shortcuts
Conversation memory

Extract05

Extraction & enrichment

Pull structured data out of free text or documents — form-fill, lead enrichment, invoice line items — with strict JSON schemas.

Schema-locked outputs
Multi-modal sources
Confidence + audit log

Search06

Semantic & vector search

Replace keyword-only search with meaning-based ranking — across products, docs, tickets or your product catalogue.

Hybrid vector + keyword
Personalised re-ranking
Drop-in API

Under the hood

Streaming UX

Token-by-token streaming, optimistic UI, smart skeletons. The feature feels live from the first 200ms — not after a 4-second wait.

First-token <300ms target
Cancel & regenerate
Graceful degradation

Smart routing

Cheap models on simple work, frontier models on the hard parts. Automatic fallback to a second provider when the first throttles or fails.

Per-feature model policy
Provider fallbacks
Prompt + response cache

Guardrails

Prompt-injection defence, schema-validated outputs, refusal templates and an adversarial eval suite that runs on every prompt change.

Input sanitisation
JSON-schema validation
Adversarial eval set

Observability

Per-feature, per-user and per-model dashboards for tokens, latency and quality. Budgets with alerting so a runaway prompt doesn't blow the month.

Token + $ per feature
Quality regression alerts
Budget caps with paging

Selected work

View all work

AI Automation & Content - Delhi

SEO Autopilot AI Engine

CRM & Enterprise SaaS - Delhi

Studio Photo Palace CRM

Media & Production - Delhi

AS Media Agency

What you get

8 items, none of them extra

Quoted once. Delivered in full.

Streaming-first UX patterns built into your design system

Version-controlled prompt library with diff review

Eval suite (golden + adversarial) wired into CI

Smart routing across providers with automatic fallbacks

Prompt + response caching with measurable hit-rate

Prompt-injection defence + schema-validated outputs

Per-feature cost, latency and quality dashboards

30-day post-launch tuning window — prompts, evals, routing

How it runs

Week 1

Feature discovery

We sit with product, look at the user journey, and pick the touchpoints where an AI feature would actually move a metric.

Feature map · KPI targets

Week 1–2

UX spec

Streaming flows, refusal copy, fallbacks, empty states — written before the prompt. Vibes are not a specification.

Feature spec · Figma

Week 2

Eval & guardrails

Golden set + adversarial prompts + schema validation, wired before the feature touches a user.

Eval suite · Guardrails

Weeks 3–5

Build & integrate

Plumbed into your codebase, tested against your design system, shipped behind a feature flag from day one.

PRs · Feature flag

Week 6

Cost & observability

Per-feature dashboards, budget caps with paging, and routing rules tuned against the real traffic mix.

Dashboards · Budgets

Ongoing

Roll out & evolve

Staged rollout, quality regression alerts, quarterly retros on what the feature should learn next.

Rollout plan · Retro

01
Week 1
Feature discovery
We sit with product, look at the user journey, and pick the touchpoints where an AI feature would actually move a metric.
Feature map · KPI targets
02
Week 1–2
UX spec
Streaming flows, refusal copy, fallbacks, empty states — written before the prompt. Vibes are not a specification.
Feature spec · Figma
03
Week 2
Eval & guardrails
Golden set + adversarial prompts + schema validation, wired before the feature touches a user.
Eval suite · Guardrails
04
Weeks 3–5
Build & integrate
Plumbed into your codebase, tested against your design system, shipped behind a feature flag from day one.
PRs · Feature flag
05
Week 6
Cost & observability
Per-feature dashboards, budget caps with paging, and routing rules tuned against the real traffic mix.
Dashboards · Budgets
06
Ongoing
Roll out & evolve
Staged rollout, quality regression alerts, quarterly retros on what the feature should learn next.
Rollout plan · Retro

The stack

Tools in this stack

Anthropic ClaudeReasoning

OpenAIReasoning

Vercel AI SDKStreaming

LangChainOrchestration

TypeScriptLanguage

ReactUI

TailwindStyling

SupabaseState + cache

Anthropic ClaudeReasoning
OpenAIReasoning
Vercel AI SDKStreaming
LangChainOrchestration
TypeScriptLanguage
ReactUI
TailwindStyling
SupabaseState + cache

FAQ

01How is this different from just calling the API?: Calling the API is the easy 10%. Production AI features need streaming UX, fallbacks, eval suites, prompt-injection defence, schema-validated outputs, caching, routing across providers and per-feature cost dashboards. We build all of that — not just the prompt.
02Which models do you use?: Whatever fits the feature. Anthropic Claude and OpenAI for most reasoning, open-weights for cost-sensitive work, smaller fine-tuned models where they outperform frontier ones. Smart routing chooses per-call with fallback to a second provider if the first throttles or fails.
03How do you control costs?: Prompt caching, response caching, smaller models on simpler calls, per-feature budgets with paging when they trip, and dashboards that show $$ per feature, per user and per model. We catch runaway spend before invoices do.
04How do you handle prompt injection?: Input sanitisation, system-prompt isolation, JSON-schema validation on every model output, and an adversarial eval suite that runs in CI on every prompt change. New attacks get added to the suite — once they pass once, they can't regress.