Catches tired-reviewer misses
A banned term or a missing disclaimer gets flagged automatically, closing the gap that opens up when a person is checking every draft manually against a mental list on a busy day.
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The whole team
Nineteen specialists, each with a defined job and an honest status label.
See all nineteenThe business catches off-brand wording (banned terms, wrong tone, missing disclaimer) before a piece goes out, without a human re-reading every line against a checklist.
Works with
What it does
The business defines rules once (words to avoid, required disclaimers, tone boundaries), and every generated or pasted draft is checked against those rules automatically. Violations are shown inline with the specific rule that was broken and a suggested fix.
A banking-adjacent business has a list of words its compliance team never wants in marketing copy — "guaranteed," "risk-free," a handful of others — and a required disclaimer line that has to appear on certain content types. Catching a violation by having a person re-read every draft against a mental checklist doesn't scale past the first few pieces of copy a week, and it's exactly the kind of check a tired reviewer misses on a Friday afternoon.
This lets a business define its rules once — banned words, required disclaimers, tone boundaries — and checks every generated or pasted draft against them automatically, showing violations inline with the specific rule broken and a suggested fix, so a person reviews what's actually flagged rather than reading every line against a checklist from memory.
Lekha runs this directly on the platforms your customers already use — no separate app for them to install.
How it works
Set banned or required terms, disclaimer requirements, and tone boundaries at the organisation level, giving Lekha a concrete, specific standard to check every future draft against consistently, rather than relying on each writer's own private understanding of what's acceptable.
A generated or pasted piece of copy is checked against the saved rule set before it's considered final, catching a banned term or a missing disclaimer before a person even starts reading through it looking for exactly that kind of mistake.
A flagged issue names exactly which rule was broken and where in the text, rather than a vague overall warning that leaves the specific problem to be hunted down manually by hand across a long piece of copy.
Where possible, a specific fix accompanies the flagged violation, turning the check into an actionable correction rather than only a warning with no clear next step attached, saving a reviewer the separate work of figuring out the right replacement wording.
Why it matters
A banned term or a missing disclaimer gets flagged automatically, closing the gap that opens up when a person is checking every draft manually against a mental list on a busy day.
The compliance team's word list and disclaimer requirements are set a single time and checked consistently on every draft afterwards, rather than re-explained to each new writer.
A suggested correction alongside each violation turns the check into something actionable, rather than a warning that still leaves the actual fix to be worked out separately by hand.
The detail
The calibration of the rule set itself determines whether this actually gets used, and it's a genuinely delicate balance in both directions. A rule set that's too strict — flagging ordinary phrasing alongside genuine violations — produces constant false-flag fatigue, and a team that gets used to dismissing flags because most turn out to be nothing eventually starts ignoring the tool altogether, including on the rare occasion it catches something that matters. A rule set that's too loose misses real violations by design, defeating the purpose of the check.
The fix isn't a one-time setup decision but an ongoing calibration process — starting with a smaller, clearly justified set of rules with good examples attached, then expanding carefully as real drafts reveal what actually needs catching, tends to produce a rule set a team trusts, rather than one drafted all at once in the abstract and either too permissive or too restrictive from day one.
It's also worth being clear about what this catches and what it doesn't: exact-term rules — a specific banned word appearing anywhere in the text — are checked reliably and mechanically, while tone-boundary rules require a more judgment-based pass that's inherently less precise than a straightforward word match. A rule like "never sound pushy" is checked with more genuine uncertainty than "never use the word guaranteed," and a business relying heavily on tone-based rules for its most serious compliance requirements should pair this check with actual human review, rather than treating a passed tone check with the same confidence as a passed exact-term one.
Industry use cases
12 industries where Lekha applies this directly.
A used-car dealer pastes the make, model, year, and mileage for a new arrival, and Lekha drafts a listing description plus a short social caption, both flagging that any claim about condition or history beyond the given facts needs the dealer's own confirmation before posting.
See the automotive playbookA sales manager sets up a persona for "IT decision-maker at a mid-size manufacturer," and every rep on the team generates cold-email drafts from the same persona so outreach feels coordinated rather than ad hoc.
See the b2b sales playbookA regional NBFC drafts a loan-product social post, and Lekha flags the line mentioning an interest rate range as a regulated claim, holding the post in a pending-review queue until the compliance officer confirms the number matches the current approved rate sheet.
See the banking and finance playbookA skincare brand uploads its full product line as a spreadsheet with ingredients and benefits per item, and Lekha generates a description and a caption for each product in one bulk run instead of one at a time.
See the beauty and cosmetics playbookA test-prep institute asks Lekha for a blog post targeting the keyword "best online coaching for class 10 boards," and Lekha returns a structured draft with headings and an FAQ section, with any specific outcome claim about pass rates flagged for the institute to verify before publishing.
See the education playbookA physiotherapy clinic drafts a social post about a new treatment offering, and Lekha flags the phrase implying guaranteed pain relief as a health claim needing review, holding it until the treating physiotherapist approves the wording.
See the health and wellness playbookA furniture retailer uploads a new collection's specs and Lekha drafts descriptions for the e-commerce listing plus a matching Instagram caption set, keeping material and dimension details exactly as supplied.
See the home decor and furnishing playbookAn agency managing five retail clients keeps five separate brand-voice profiles in Lekha, so the same campaign-brief-to-assets workflow produces distinctly different-sounding output for each client from the same underlying template.
See the marketing agencies playbookA broker pastes a property's square footage, bedroom count, and location, and Lekha drafts a listing description and a short email to interested buyers, both built strictly from the supplied facts.
See the real estate playbookA restaurant launching a new seasonal menu pastes the dish names and ingredients, and Lekha generates a description for each dish plus a launch-announcement caption, ready to review before the menu print deadline.
See the restaurants and food playbookA day spa asks Lekha to write copy for a new facial package, and Lekha drafts the description but flags a line implying a specific skin-improvement result as needing the owner's confirmation before it's used in an ad.
See the spas and salons playbookA tour operator asks for a Kerala backwaters package description in both English and Hindi, and Lekha produces two natural-reading versions rather than a literal translation of one into the other.
See the travel and tourism playbookMore from Lekha
The business gets copy that consistently sounds like them, in every language, without re-explaining tone in every prompt.
Learn moreThe business drafts a usable first version of an ad, caption, email, or product description in seconds instead of starting from a blank page.
Learn moreThe business can dial a single draft from formal to playful (or vice versa) without rewriting it from scratch.
Learn moreThe business reaches Hindi-speaking and code-mixed audiences with copy that reads naturally, not like a literal translation.
Learn moreThe business turns one piece of English copy into ready-to-use versions in other languages without hiring a translator for routine content.
Learn moreThe business gets a blog post or landing page draft built around a target keyword, ready to publish with minimal editing.
Learn moreQuestions
This creates a real risk called false-flag fatigue — a team that starts seeing mostly harmless flags gets used to dismissing them, and eventually starts ignoring the tool altogether, including on the rare occasion it catches something that genuinely matters. Starting with a smaller, well-justified rule set and expanding it carefully based on real drafts, rather than an exhaustive list drafted all at once, tends to avoid this.
No — an exact-term rule, like a specific banned word, is checked mechanically and reliably, while a tone-boundary rule requires a more judgment-based assessment that's inherently less precise. A business relying on tone rules for its most serious compliance needs should pair this check with an actual human review for anything genuinely high-stakes, rather than trusting a passed tone check with the same confidence as a passed exact-term match.
It's built to catch what a person checking manually against a mental checklist is likely to miss, particularly at volume, but it isn't a substitute for compliance judgment on genuinely high-stakes content, especially where tone-based or context-dependent rules are involved rather than a simple exact-term match. It reduces the volume of routine checking a person needs to do manually, rather than removing the need for review entirely.
The business defines its own rules, and while an initial rule set is needed to begin with, ongoing calibration based on what real drafts actually reveal — a rule that's too strict, or a gap that let something through — tends to produce a rule set the team actually trusts over time. Treating it as a one-time setup rather than an occasionally revisited list is the more common way this ends up either too noisy or too permissive.
The rest of your stack
No rip-and-replace — check copy against brand rules works alongside the systems already running your business.
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