Runs behind your software
Connects over REST to your e-commerce, CRM, ERP, SaaS or mobile application.
Text analysis, customer review scoring, content moderation, summarisation, audio transcription and smart search connect to the systems you already run through one API layer. No model of your own to build, train or host.
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“The quality is far better than I expected and delivery was quick. Highly recommended.”
Rated 5 stars
What comes back
Connects over REST to your e-commerce, CRM, ERP, SaaS or mobile application.
Not a fixed black box: you set your own evaluation and critical criteria.
The analysis becomes an operational call such as publish or do not publish.
On the API side Ai Thinks never talks to the end user. It is the AI analysis and decision layer running behind other software: your system sends the data, the platform analyses it, and the result comes back in the same flow.
Your e-commerce, CRM, ERP, SaaS or mobile app passes the content to be analysed to the API.
The content is scored against the evaluation criteria, custom filters and weightings that you defined.
The structured output and the publishing decision come back in the same call or over a webhook.
When a customer review reaches the platform, every check you defined runs inside the same analysis and resolves into a single decision.
Customer review
“Delivery was very quick and the product matches the description. Great value for the price.”
Rated 5 stars
The decision comes from the rules you set. This review clears every critical criterion and scores above your threshold, so it goes live; had one criterion come back negative, or had the rating contradicted the review, the same request would have returned a hold decision instead.
Every service can be called on its own, chained to another, and authorised separately for each company.
Scores customer reviews against evaluation criteria, critical criteria, the star rating and language quality — including whether the rating and the review actually agree.
Outputs
Evaluates guest reviews against quality criteria, competitor mentions, abusive language and content that may belong to a different property, then returns a publishing decision.
Outputs
Evaluates large volumes of customer reviews together and produces either a single overall summary or a summary broken down by category.
Outputs
Detects profanity and inappropriate language in text, and works as a moderation layer in any system where users produce content.
Outputs
Flags whether a review or a piece of text promotes another brand, advertises an external site, or steers the reader somewhere else.
Outputs
Checks whether the text carries political wording or political context, and returns it as a separate signal for systems that need moderation.
Outputs
Turns an audio recording into text: separates the speakers, splits the recording into segments, and returns either timestamped or plain-text output.
Outputs
Turns a request written in natural language into the structured search parameters your own system can execute directly.
Outputs
Which API is opened to which company is authorised separately from the panel, so a company can only call the services defined for it.
Unlike single-review analysis, the summarisation service evaluates many customer reviews together and surfaces the opinions that keep repeating.
Reduces every review to one piece of evaluation text.
Produces a separate summary and sentiment under each heading you choose.
You decide which topics get analysed
Most users are happy with the quality of the product, though durability draws a recurring set of complaints.
The majority of reviews speak positively about how quickly the order arrived.
Damaged boxes and insufficient packaging come up repeatedly among a specific group of users.
For hotel groups, marketplaces, e-commerce companies and global SaaS applications working across borders, the output language is set with the request.
This is what separates Ai Thinks from off-the-shelf AI APIs: you define the analysis criteria, the critical criteria and the weightings, and the platform returns the outcome as an operational decision rather than raw data.
A company defines its own analysis criteria, which is how the same AI infrastructure adapts to the business rules of very different industries.
Filter name
Competitor brand check
Evaluation criteria
Analyses do not have to work as a simple yes/no. You assign a weight to each criterion and let the decision follow a minimum average score.
The company decides which of these rules apply.
That turns Ai Thinks from something that merely analyses into an API layer that produces decisions from your business rules.
Call centre recordings, conversations and meeting audio become text: speakers are separated and every segment is marked with a start and end time.
This chain is one possible way of composing the existing services; each step is a separate API call.
Takes what a user expressed in a sentence and converts it into search parameters your own system can execute directly.
What the user typed
“Find a family hotel in Antalya between 15 and 20 August for two adults and one child, within a certain price range.”
Generated search parameters
Supported search criteria
The parameter model is mapped to hotel and travel data; the same structure can be extended to your own data model.
The APIs are never left openly accessible. Which company may use which service, and where the analysis result should go, are defined from a single panel.
Not every company needs every API; permissions are granted and revoked per company.
Company A
Company B
You define the address the result should return to, so long-running analyses do not force the external system to keep polling the API.
Webhook configuration
The platform calls you once the result is ready — no open connection to hold.
You do not have to build on the services from scratch. Start from scenarios already proven in the field and adapt them to your own business rules.
We review your data sources, your analysis criteria and your integration scenario together, then set out a pilot plan with a test token.