TBO AI Smart Search for Indian travel agents: what it does, what it misses, and how to use it in 2026
By Vihaan Patel (Vihaan Patel covers the intersection of travel and digital payments — Indian OTAs, airline-direct booking flows, UPI vs credit-card surcharges, RBI tokenisation rules and the booking-funnel mechanics that quietly cost (or save) you money.) · Published · 11 min read
TBO — Travel Boutique Online — rolled out AI-assisted search features for their agent portal in 2025–2026, letting agents type natural-language queries rather than filling structured form fields. It sounds like a leap, and in some ways it is. In others, it is still a work in progress. Here is what actually changes for day-to-day agent workflow and where you still need to revert to the old manual method.
TL;DR — the short answer
TBO's AI Smart Search allows Indian travel agents to type natural-language queries — like 'cheapest business class BOM to JFK in October for 2 pax' — and have the system interpret the intent, pull relevant fares, and surface options without manually selecting origin, destination, date pickers, and class filters separately. In practice it saves meaningful time on multi-parameter queries and is genuinely good at surfacing flexible-date options agents would not have spotted manually. Its limitations: it does not replace fare-rule knowledge (you still need to understand what you are booking), it is less reliable on complex multi-city itineraries, and it works best on routes with good TBO inventory depth. Think of it as a fast first-pass, not a complete replacement for the structured search.
What is TBO AI Smart Search and how is it different from standard TBO search?
Standard TBO search — the one most agents have used for years — is a structured form: select origin, destination, date, class, number of passengers, then hit search. It is deterministic and reliable but requires precise inputs. If you know exactly what you want, it works well. If a client has given you a vague brief ('something in September, flexible on exact dates, looking for the best value on Bangkok flights'), translating that into structured search fields means doing multiple searches manually.
TBO's AI layer interprets a free-text input and extracts the key parameters automatically — dates (including relative ones like 'next month'), route, class, passenger count, and any other preferences mentioned. It then runs the search and presents results in a format similar to the standard search output, with fare options, airline choices, and pricing. The AI interpretation is powered by a large language model layer that sits on top of TBO's existing fare inventory and booking APIs.
In 2026, the feature is available within the TBO agent portal (you may need to enable it in settings or look for the 'Smart Search' option in the search interface — TBO has been rolling it out progressively, so exact placement in the UI varies by account version). Check the TBO agent portal announcement section or contact your TBO account manager if you cannot locate it.
What does TBO AI Smart Search actually do well?
The use cases where the AI search genuinely saves time and surfaces value:
- Flexible-date queries: 'Best fares for Bangalore to London in the last two weeks of November' — the AI interprets this as a date range, runs multiple date checks, and presents the cheapest options across that window. This would take an agent four or five manual searches in the standard form. The AI collapses it into one query.
- Class-of-service shortcuts: Natural phrases like 'business class options under ₹1.5 lakh' are interpreted correctly. The AI filters by cabin and applies a rough fare ceiling, surfacing only relevant results. Useful when a client has budget in mind but no airline preference.
- Route interpretation: Agents note the AI handles common city name variations and IATA code ambiguities well. 'Delhi to Dubai' versus 'DEL-DXB' both work. Less certain for smaller secondary cities — the AI can sometimes misinterpret an ambiguous city name.
- Hotel + flight combination queries: TBO is a full-service B2B portal offering both, and the AI search can handle a combined query ('flight and hotel for 4 nights Bangkok October') to bring up both inventory types in a single flow, reducing the back-and-forth between tabs.
- Saving typical client briefs: Once you know what the AI understands, you can develop a shorthand for your most common client types — 'family economy return DEL to DXB school holidays Jan' — and run it repeatedly without reformatting.
Where TBO AI Smart Search still falls short
Honesty matters here because over-relying on the AI for edge cases creates booking errors:
- Complex multi-city itineraries: A classic open-jaw or multi-city itinerary with different return city — 'fly Mumbai to Paris, return from London to Mumbai via Istanbul' — tends to trip the AI up. The natural-language interpretation breaks down on non-standard routing logic. Revert to manual structured search for anything more complex than a round-trip or simple one-way.
- Fare rule interpretation: The AI surfaces fares but does not explain fare rules in detail. A '14-day advance purchase, non-refundable, change fee ₹X' fare will appear in results, but you still need to click through to the fare rules and apply your own knowledge of whether it suits the client. Never skip the fare rule check just because AI presented the option.
- Group and series fares: TBO's group fare inventory (for bookings of typically 10+ passengers) is a separate workflow, and the AI Smart Search does not consistently surface group-negotiated fares or series fare blocks. For group bookings, use TBO's dedicated groups desk.
- Real-time net fare comparisons: The AI presents TBO's inventory, not a cross-portal comparison. For serious net-fare hunting across multiple portals, using a tool like FlightGPT for a quick metasearch comparison before committing to TBO is worthwhile — AI search within a single portal is inherently limited by that portal's inventory.
- Recent inventory changes: If an airline has just updated its fare structure or added/removed a route, there can be a short lag before TBO's inventory reflects it. AI interpreting a query correctly but pulling slightly stale data is a rare but real edge case.
Practical workflow: how to integrate AI Smart Search into daily agent operations
A suggested workflow that gets the most out of TBO AI search without over-relying on it:
- Use AI search for the first pass. When a client brief comes in, type a natural-language query and scan the results. This gives you a fast sense of the fare landscape — price range, airline options, rough availability — in 30 seconds rather than three minutes of form-filling.
- Cross-check with structured search on the top options. Once the AI search has surfaced two or three interesting fares, go into those specific flights via the standard search to verify availability, check fare rules, and confirm what you are looking at is actually bookable at the displayed price. AI search prices are directionally correct, but always confirm before presenting to the client.
- Use the standard search for anything complex. Multi-city, group bookings, or fare classes you need to book on a specific sub-fare basis — go structured. AI is a time-saver for simple queries, not a replacement for technical booking workflows.
- Save your best natural-language templates. Keep a notes document with queries that worked well for common client types. Reusing a tested query format is faster than composing fresh each time.
As AI booking tools evolve across portals, the underlying dynamic is the same one playing out in consumer flight search: the AI is a filter and interpreter, not an oracle. The agent's value add remains fare-rule knowledge, client relationship management, and recognising when a result that 'looks right' needs to be double-checked. That is not going away any time soon.
If you are looking to benchmark TBO's fares against other sources as part of your standard research workflow, FlightGPT Partner and flightgpt.in are useful tools for a quick cross-platform check before committing to a booking.
How does TBO AI compare to what other Indian B2B portals are doing?
TBO is ahead of eTrav and most second-tier Indian B2B portals in deploying a visible AI search layer as of 2026. Tripjack has been rolling out smart features in their search interface but has not prominently marketed an AI natural-language search in the same way. Most of the public conversation about AI in Indian travel tech is still concentrated on the consumer side — ChatGPT-style assistants for travellers — rather than agent-side tooling.
The honest picture: all the major portals are building or piloting AI-assisted features. TBO has moved fastest among the traditional B2B players. But the B2C AI flight search — tools like FlightGPT that give consumers a natural-language interface to search across multiple sources — is arguably further along in terms of multi-source comparison capability, because they are not constrained to one portal's inventory.
For agents, the relevant question is not 'which portal has the best AI' but 'which portal has the deepest inventory and best net fares for the routes my clients book most?' AI search is a UX improvement on top of existing inventory — it does not change the underlying economics. Also worth reading: Tripjack credit limit management and BSP dispute processes for the operational side of agent platform management.
Frequently asked questions
Is TBO AI Smart Search available to all Indian travel agents on TBO?
As of 2026, TBO has been rolling out the AI search feature progressively — it may not be visible on all agent accounts yet, particularly older account types. Log in to the TBO portal and look for a 'Smart Search' option or a free-text input field. If you cannot see it, contact your TBO account manager to request access or check for a UI upgrade.
Can TBO AI search handle queries in Hindi or regional languages?
As of mid-2026, TBO AI Smart Search is primarily English-language. The AI interprets English natural-language queries well. Hindi or mixed Hindi-English ('Hinglish') queries may work for simple route and date recognition but are less reliable for complex fare or class specifications. Stick to English for most accurate results.
Does the AI search affect the net fare prices shown, or is it just a search filter?
The AI search is a search and filter layer — it does not change the underlying net fares TBO offers your agency. The same fares available via structured search are available via AI search. The AI makes it faster to find them; it does not alter pricing. Your negotiated net fare rates and credit terms remain unchanged.
How does TBO AI search compare to searching on GDS like Amadeus?
GDS (Amadeus, Sabre, Galileo) offers deeper fare rule control, historical pricing access, and wider carrier availability for agents booking international itineraries — particularly for full-service carriers. TBO's AI search is faster and more intuitive for straightforward bookings but does not match GDS depth for complex international fares or low-cost carrier consolidator inventory. Many agents use both: TBO for quick domestic and hotel bookings, GDS for complex international ticketing.
What should I do if TBO AI search shows a fare that cannot be confirmed at booking?
This is a known edge case with any AI-assisted search — the displayed fare is pulled from live inventory, but by the time you proceed to booking, the seat may have sold out or the fare may have expired. Always proceed to the booking confirmation step quickly after finding a fare you want, and never present a price to a client as confirmed until you have a PNR and ticket number. The lag between AI search result and booking is typically seconds, but on popular routes during peak season it matters.
Is TBO AI search good for finding error fares or unusual routing discounts?
Not reliably — TBO AI search queries TBO's standard fare inventory, which is unlikely to surface error fares or unusual consolidator discounts that might exist on other platforms. For error fares and unusual routing opportunities, cross-platform metasearch tools and dedicated fare-alert communities are more effective. The AI search excels at standard inventory with natural-language convenience, not fare arbitrage.