Aichitect is an AI-first London startup that builds an AI co‑pilot for planning and early-stage development decisions, aiming to de‑risk and speed up planning permission and construction feasibility for architects, developers, local authorities and homeowners[3][1].
High-Level overview
- Mission: Aichitect’s stated mission is to de‑risk, decarbonise and optimise construction and planning workflows by delivering evidence‑backed, fast intelligence to stakeholders across development and planning[1][3].- Investment philosophy / (if answering as a portfolio company): Aichitect is a venture‑backed startup that raised a £300k pre‑seed round led by SFC Capital to scale its AI platform and expand in the UK and internationally[2].- Key sectors: The company targets the built‑environment stack — architecture, property development, local government planning, consultants and homeowners involved in planning applications[1][3][4].- Impact on the startup ecosystem: By automating planning risk assessment and making planning outcomes more predictable, Aichitect reduces friction for small developers and architects, shortens feedback cycles for authorities, and creates demand for data‑driven planning tools in proptech and govtech markets[3][1].
For a portfolio company (product / customers / problem / momentum)
- Product: An AI platform that predicts planning permission outcomes, provides a project risk score, and issues targeted recommendations and “planning verdicts” to guide submissions and designs[3][1].- Who it serves: Architects, property developers, planning consultants, local planning authorities and some homeowners seeking planning permission[3][1].- Problem it solves: High first‑submission failure rates and inconsistent policy interpretation across boroughs that cause costly redesigns, delays and wasted consultancy fees by giving instant, confidence‑backed planning forecasts and compliance checks[3].- Growth momentum: Founded in 2022, Aichitect joined accelerators and raised £300k in pre‑seed funding in April 2025 led by SFC Capital to grow its team and product offering and to pilot projects beyond the UK[2][6][4].
Origin story
- Founding year and team: Aichitect was founded in 2022 by David Adjei, a chartered architect and property developer with experience as a RIBA councillor and Homes England assessor, and co‑founders including technical leads with AI background such as Dr Simon Wallace[3][4].- How the idea emerged: The founder describes the product arising from firsthand experience of costly planning delays and inconsistent interpretations of planning policy in the UK, motivating a data‑driven, automated approach to predict outcomes and streamline compliance[6][3].- Early traction / pivotal moments: Early recognition includes acceptance into accelerator programs (e.g., Black Valley) and publicised pre‑seed funding led by SFC Capital in 2025 that validated market fit and enabled product and geographic expansion[6][2].
Core differentiators
- Domain expertise + product focus: Founded by a chartered architect with planning experience, Aichitect pairs deep domain knowledge with AI models trained on planning outcomes to offer specialized predictions versus generic ML tools[3][4].- Planning‑specific accuracy claims: The company markets high accuracy for planning verdicts (examples on the site claim 97% accuracy and instant analysis), positioning itself as an actionable decision tool rather than an advisory report[3].- Workflow integration & SaaS packaging: Aichitect offers instant checks, an AI planning assistant, enhanced verdicts, API possibilities and team plans for repeat customers and government usage, supporting both single projects and enterprise integrations[3][1].- Time and cost impact: By identifying likely refusal risks and compliance gaps early, the platform promises to reduce redesign cycles, consultancy costs and planning delays that commonly inflate project budgets and timelines[3][1].
Role in the broader tech landscape
- Trend alignment: Aichitect rides the convergence of AI, proptech and govtech where ML is applied to domain‑specific regulatory decisions and operational bottlenecks in the built environment[1][3].- Why timing matters: Rising planning complexity, higher developer risk exposure, and increasing appetite from authorities for digital tools create market pull for automation that reduces backlog and standardises reviews[3][1].- Market forces in its favor: Demand for faster, cheaper pre‑application validation, pressure to decarbonise construction (which benefits from early feasibility and compliance checks) and investor interest in verticalized AI SaaS all favor growth for focused solutions like Aichitect[1][2].- Influence on ecosystem: If broadly adopted, the product could shift how small developers and architects approach submissions (more data first), reduce burden on planning officers, and spur more specialized AI services for other regulatory domains in real estate and infrastructure[3][1].
Quick take & future outlook
- What’s next: With the 2025 pre‑seed, Aichitect will likely iterate its models, extend dataset coverage across more UK boroughs, pursue integrations with planning systems and pilot projects in regions such as the Middle East as indicated by its funding plan[2][3].- Trends that will shape its journey: Model accuracy improvements, access to more labelled planning outcome datasets, local government digitisation programs, and regulatory scrutiny of AI‑driven decisions will all affect product adoption and defensibility[3][1].- How influence might evolve: Success depends on balancing transparency and explainability (to satisfy planning authorities), scaling data partnerships for robust training, and proving measurable reductions in time/cost for customers — outcomes that could make Aichitect a standard pre‑submission tool for developers and planning teams[3][2][1].
Quick reiteration: Aichitect is a specialist AI planning co‑pilot founded in 2022 that combines architectural domain expertise with ML to predict planning outcomes, reduce project risk and speed decision‑making across the construction and planning value chain[3][1][2].
