The Problem: Buried Project Photos, Lost Insights
Picture this: you’re kicking off a new design and recall a perfect facade detail from a past project – if only you could find that photo buried in the server. You’re not alone. In many architecture and construction firms, valuable project photos and documents sit scattered in countless folders, effectively invisible when you need them most.
Traditional file systems and basic search tools often force you to remember exact filenames or dates. Important lessons and design ideas languish in archives, leading teams to repeat mistakes or reinvent the wheel because past lessons aren’t easily accessible. It’s a huge efficiency problem – in fact, surveys show around 13% of a construction professional’s working hours are spent just searching for project information, a waste that causes delays and missed opportunities.
This is exactly the problem Tektome’s KnowledgeBuilder was created to solve. What used to be buried in folders is now instantly searchable – the platform connects past decisions and insights to current challenges by transforming your disjointed project files into a readily searchable knowledge base.
How KnowledgeBuilder Works (in Short)
KnowledgeBuilder uses artificial intelligence to extract key information from unstructured files – drawings, documents, and photos – and turn it into a structured database of project knowledge. Think of it as an AI librarian that scans every image and file, then tags and indexes their content so you can search for them by meaning, not just filename.
For example, if you upload a site photograph, KnowledgeBuilder will analyse the image and record attributes: is it an interior or exterior? What elements or materials are visible? It might note that a file named IMG_7532.HEIC is an exterior shot of a high-rise with a glass facade. An image of a lobby might be tagged as interior with keywords like “double-height, oak, elevator hall,” describing its spatial and material features. The AI even assigns richer metadata – for instance, recognizing the “mood” or atmosphere of images (e.g. bright, cozy, open) – enabling new ways to search your visual data. All this happens behind the scenes when you add your project files.
Crucially, you don’t have to pre-define all possible tags or hire someone to manually catalogue thousands of photos. KnowledgeBuilder’s model is flexible and learnable: you can configure what attributes to extract, and it will apply those rules to incoming files automatically. By building this organised index of everything – project locations, building types, materials used, common issues, and more – the platform makes previously hidden details searchable in plain language.
Practical Use Cases: From Early Inspiration to Detailed Design
How does this AI-driven image search help day-to-day architectural work? Let’s explore a couple of scenarios across a project’s timeline.
Early-Stage Inspiration: Search Past Projects Visually
In the early phase of design, architects often seek inspiration from precedents – looking for ideas on form, style, material, or solutions to site constraints. KnowledgeBuilder supercharges this process. Because the system has structured your past project photos alongside other data, you can search across all projects at once, using whatever criteria matter to you. For example, you could filter past projects by attributes like location, building type or scale to narrow the field. Then, query the photos within those projects using natural language: “modern glass curtain wall on a high-rise”. Instantly, you’d see images of tall buildings with sleek glass facades from your firm’s archives that match that description.
Need interior design inspiration? Simply describe it. You might search for “bright, spacious retail interior” to retrieve photographs of vibrant commercial spaces that your team has done before. KnowledgeBuilder understands the query and might pull up, say, a shopping centre atrium photo tagged with “Interior – retail, double-height, skylight, bright”. You can even hunt for images by mood or style. Because the AI analyses qualitative aspects, you could ask for “cozy lobby with wood accents” or write keywords such as “glass” and “greenery” and get relevant images.
If you’re interested in how the data gets extracted, check out our article here.
Later-Stage Detailing: Learn from Visual Lessons and Mistakes
As the project moves into detailed design, KnowledgeBuilder’s searchable photo archive becomes just as invaluable. Now the questions get specific: How did we detail that feature last time? Has anyone dealt with this layout quirk or material issue before? With a unified visual knowledge base, you can rapidly find out.
For instance, an architect detailing a stair lobby could search “staircase design with skylight” to see images of staircase and atrium combinations from past jobs. If you’re working on a facade detail, you might look up “timber cladding joint” and retrieve photos (and perhaps annotated drawings) showing how a wood cladding detail was executed, sparing you from starting from scratch.
Because KnowledgeBuilder links related content, those photos often come alongside their original project context – you might find the image of a beautiful handrail is connected to that project’s detail drawings or specs, which you can easily reference for dimensions or notes. All of this happens through the same search interface, saving you from rifling through old binders or emailing colleagues “Hey, do we have any examples of X?”.
In one real use case scenario, a team discovered through KnowledgeBuilder an internal case of water damage where an electrical room was placed next to a wet garbage room. Normally that lesson might have been buried in an old report, but by searching a phrase like “electrical room next to wet room,” the architects instantly pulled up the past case – complete with the fix (a raised floor curb and improved waterproofing). They made design adjustments early, avoiding a costly mistake.
Value to Teams: Faster Insights, Fewer Mistakes
For architecture and engineering teams, the benefits of this approach are game-changing. First, there’s a huge time savings: what used to take hours of combing through archives can now take seconds, whether you’re looking for an inspiring image or a specific past fix.
By spending minutes instead of days to find the right detail, teams free up time to focus on actual design work. And because search is both broad and deep (covering every project and file type, with AI understanding context), you get more accurate results – you’re likely to find the best reference, not just the one person happen to recall. This translates to better-informed decisions.
Perhaps most importantly, KnowledgeBuilder helps create a culture of continuous improvement. When past decisions and insights are connected to today’s challenges, it fuels smarter design and fewer repeated mistakes.
Summary
In an industry where images speak louder than words, having a tool that makes every photo and document in your archive searchable is like gaining a superpower. Tektome’s KnowledgeBuilder tackles the long-standing knowledge gap in AEC by turning your project data – including those interior and exterior photos once lost in the depths of your server – into live resources for your team.
If you’re ready to make your photos and files work for you it might be time to give KnowledgeBuilder a try. Explore the KnowledgeBuilder page or schedule a demo to see this AI-powered system in action. Don’t let your team’s collective knowledge go to waste in forgotten folders. Turn those past interiors and exteriors into searchable insights today – your next project (and your future self) will thank you.
