Intouch Insight Ltd. reported second-quarter revenue of CAD 7.02 million, an 8% organic increase from CAD 6.50 million in the same period last year, marking the strongest quarterly growth in seven years. The Canadian software provider, which operates under the In-Touch Survey Systems brand, also posted a net loss of CAD 43,136 compared with a CAD 1.11 million loss in Q2 2025.
Year-to-date revenue reached CAD 13.69 million, up 7% from CAD 12.82 million in the first half of 2025. Adjusted EBITDA fell to CAD 227,559 from CAD 370,812 a year earlier, while gross profit remained nearly flat at CAD 3.28 million. Gross margin contracted by 370 basis points to 46.7%.
U.S. operations drove the growth, generating CAD 5.84 million in revenue for the quarter, a 15% increase. Canadian revenue declined 18% to CAD 1.17 million, and international revenue totaled CAD 7,197. Recurring services accounted for CAD 5.88 million, up 6% year-over-year, while SaaS revenue rose 18% to CAD 475,000. Merchandising revenue, a new segment, contributed CAD 82,824, with management projecting third-quarter merchandising sales to exceed the first-half total.
Operating expenses increased, with selling expenses up 24% to CAD 845,670 and product development costs rising 20% to CAD 561,293. General and administrative expenses fell 7% to CAD 1.81 million, aided by a CAD 93,957 foreign exchange gain. Cash used in operating activities totaled CAD 403,631 for the quarter, bringing the year-to-date outflow to CAD 598,857.
The company maintained its full-year guidance for double-digit organic revenue growth and reiterated a merchandising revenue target exceeding CAD 1 million. Intouch ended the quarter with CAD 954,386 in cash, down CAD 644,774 from year-end, and utilized CAD 1.97 million of its CAD 2.6 million credit facility.
Cameron Watt, president and CEO, stated that the 8% revenue growth represented the company's strongest quarterly performance since 2019. Cathy Smith, CFO, attributed receivables timing to the net loss, noting no change in revenue quality and expected normalization in collections.
