What problem did Quikdu's AI content engine have?

The n8n workflow that generates Quikdu's SEO Ayurveda blogs was tied directly to a single AI provider, with no fallback model, no automated quality checks and limited cost visibility, so a model deprecation, price change or outage could stop content publishing.

What does an AI rescue and migration involve?

It involves taking over the affected AI workflow or codebase, stabilising it quickly, migrating to a stable replacement model, and rebuilding the AI layer to be model-agnostic with fallbacks, evaluation and monitoring so the same failure cannot happen again.

How long did the rescue take?

The engagement followed ELITIST's 2 to 4 week stabilisation approach: triage and takeover, model evaluation and migration, gateway and fallback implementation, then testing, monitoring and handover.

What is a model-agnostic AI gateway?

It is a central interface that the workflow calls instead of a specific vendor's API. Behind it, different models can be used, swapped or added through configuration, with automatic fallback when a model fails.

Did switching models change the content quality?

Candidate models were tested on real Quikdu product topics before go-live, prompts were rewritten for each model, and a structured output format was enforced, so content quality and format stay consistent across models.

How is AI-generated health content kept accurate?

The workflow now validates PubMed Central reference links, flags risky health claims and includes a human review step before publishing.

Did the migration help control AI costs?

Yes. The gateway logs cost per blog, and lighter tasks are routed to lower-cost models with caching and leaner prompts, keeping AI spend predictable as volume grows.

Can Quikdu change AI models in the future without help?

Yes. Model changes are a configuration update tested against the evaluation suite, and the architecture, prompts and runbooks are documented for the team.

Which businesses need an AI rescue like this?

Any business whose products or operations depend on a single AI vendor, including ecommerce brands using AI for content and support, SaaS companies with AI features, and companies whose AI was built by an agency or freelancer who is no longer involved.

How can ELITIST SOFTWARE SOLUTIONS LLP help with AI rescue and migration?

ELITIST SOFTWARE SOLUTIONS LLP offers a fixed-scope 2 to 4 week AI rescue and migration package: codebase or workflow takeover, rapid stabilisation, migration to stable or open-source models, a model-agnostic AI gateway with fallbacks, quality evaluation, cost controls, monitoring and full handover.

Case Study

AI Vendor Rescue: Restoring Quikdu's AI Content Engine in 3 Weeks

How ELITIST SOFTWARE SOLUTIONS LLP stabilised Quikdu's n8n-powered AI content engine, removed its dependency on a single AI provider and rebuilt it on a model-agnostic architecture with automatic fallback models, quality checks and cost controls.

Workflow Takeover

Mapped every AI call in the n8n workflow, secured credentials and exported prompts and logs.

AI Gateway

Replaced direct vendor calls with one internal interface for every model.

Smart API System

Fallback Models

Automatic switching to backup models on failures, slowdowns or rate limits.

CLIENT

Quikdu Solutions Private Limited

Industry

HealthTech • Ecommerce • SaaS

Delivered Services

AI Rescue & MigrationAI Workflow StabilisationModel-Agnostic ArchitectureLLM MigrationAI Gateway Developmentn8n Workflow EngineeringAI Quality EvaluationAI Cost OptimisationMonitoring & AlertingDocumentation & Handover

Challenge

Quikdu relies on an automated n8n workflow to generate SEO-optimised Ayurveda product blogs with PubMed Central references, supporting organic growth for its website and app. The workflow was wired directly to a single AI provider, with prompts, output formats and error handling built around one specific model. That made the content engine fragile: a model deprecation, a rate limit, a price change or a provider outage could stop publishing entirely, and output quality could shift without warning whenever the provider updated its model. There was no fallback model, no automated way to check content quality or citations before publishing, and limited visibility into what each blog cost to produce. Quikdu needed the engine stabilised quickly and rebuilt so that no single AI vendor could hold its content pipeline hostage again.

Solution

Emergency Triage & Workflow Takeover

Mapped every node and AI call in the n8n content workflow, secured API keys and credentials, exported prompts, sample outputs and logs, and identified each point where a provider failure would stop the pipeline.

Rapid Stabilisation

Added retries, timeouts and clear error handling so failed AI calls no longer broke the whole run, and routed critical steps to an alternative model provider while the full migration was carried out.

Model Evaluation & Selection

Tested candidate commercial and open-weight models on real Quikdu product topics, comparing writing quality, accuracy, citation handling, structured output reliability, latency and cost before choosing a primary model and fallbacks for each step.

Prompt Migration & Output Standardisation

Rewrote and versioned prompts for the new models, and enforced a structured output format for titles, meta descriptions, headings, body content and references so downstream publishing steps receive consistent data regardless of model.

Model-Agnostic AI Gateway

Replaced direct vendor calls with a central AI gateway that the n8n workflow calls through one interface. Behind it, models can be switched, added or removed through configuration rather than code changes.

Automatic Fallback & Model Routing

Configured automatic fallback to secondary models when the primary model fails, slows down or hits rate limits, and routed lighter tasks such as meta descriptions and summaries to lower-cost models.

Citation & Health-Claim Quality Checks

Added automated checks that validate PubMed Central reference links and flag risky health claims, with a human review step before publishing, so faster automation does not come at the cost of accuracy or compliance.

Evaluation Suite for Future Model Changes

Built a set of real product topics with expected output standards, so any new or updated model can be tested against Quikdu's quality bar before it goes live and regressions are caught early.

Cost Tracking & Optimisation

Logged token usage and cost per blog through the gateway, and introduced caching and leaner prompts to keep AI spend predictable as content volume grows.

Monitoring, Documentation & Handover

Set up alerts for failed runs, fallback activations and cost spikes, and documented the architecture, prompts and runbooks so the team can switch models or extend the workflow without outside help.

Outcome

Content Engine Back to Reliable Publishing

The AI content workflow was stabilised and now handles AI errors and slowdowns gracefully instead of stopping the entire publishing run.

No Single AI Vendor Dependency

With a model-agnostic gateway and tested fallback models, a deprecation, price change or outage at one provider no longer threatens Quikdu's content pipeline.

Model Switching Without Rebuilds

Changing or adding an AI model is now a configuration change backed by an evaluation suite, rather than a development project.

Consistent Output Across Models

Standardised, structured outputs mean SEO fields, headings and references reach the website in the same format whichever model generated them.

Safer AI-Generated Health Content

Automated citation validation, health-claim checks and human review reduce the risk of inaccurate or non-compliant Ayurveda content being published.

Predictable AI Costs

Cost per blog is now visible, and routing, caching and leaner prompts keep AI spend in line with content output.

Faster Issue Detection

Monitoring and alerts surface failed runs, fallback events and cost spikes immediately, so problems are fixed before they affect publishing schedules.

Full Ownership of the AI Stack

Prompts, evaluation data, configuration and documentation now sit in systems Quikdu controls, leaving the business in charge of its AI rather than its vendors.