A consulting-grade AI built for strategy, finance, and operations decisions. Frameworks, benchmarks, and sector intelligence — in a single conversational interface designed for the way leaders actually work.
Development AI is trained on the same frameworks, sector taxonomies, and benchmark structures our consultants use day to day. It connects to your documents, your spreadsheets, and your sector data — and produces output that holds up in a steering-committee room.
You can ask it to size a market, build a financial sensitivity, draft a board memo, or stress-test an assumption against five comparable engagements. It returns structured answers with sources, not paragraphs of plausible-sounding text.
And because it was built inside a consultancy, it asks the next question. If you say "we are considering an acquisition," it won't write you an essay. It will ask which sector, which size, and what your strategic frame is — the way an analyst would.
Porter, BCG, value-chain decomposition, jobs-to-be-done, McKinsey 7S — Development AI applies the right frame for the right question, and tells you when none of them fit.
Margin structures, capex intensity, working-capital norms, and operational KPIs for 12 sectors — sourced from Kaya engagements and published filings.
Build a weighted decision matrix in three turns. Stress-test it against scenarios. Surface the assumptions that change the answer.
Upload a 200-page tender, a 12-tab model, or a regulator's response. Ask questions in plain language and get cited, traceable answers.
Generate first-draft memos in the Kaya house style, with executive summary, supporting evidence, and risks. Slides export to a clean .pptx scaffold.
Save your most-used routines — board prep, weekly KPI review, deal screening — as one-click workflows that pull from your live data.
Bring in your documents, models, and data rooms. Development AI builds a private workspace per engagement — your data never leaves it.
Ask the way you would ask a senior analyst. Development AI clarifies the question, picks the frame, and returns a structured answer with sources.
Take the answer into a memo, a slide, a model, or a decision log. Every artefact carries its sources — so the next reviewer can audit the chain.
No. We use frontier models underneath, but the consulting layer — frameworks, sector taxonomies, benchmark databases, the asking-the-next-question behavior — is proprietary. The model knows physics; we taught the layer to think like an analyst.
On Team and Individual, data is encrypted in transit and at rest, never used to train shared models, and deleted on workspace closure. On Enterprise, we deploy single-tenant in your region of choice — Türkiye, EU, US, or UK — and you hold the keys.
Benchmarks are refreshed quarterly and carry sample-size disclosure. If a benchmark is based on fewer than five comparables, Development AI tells you and offers ranges rather than point estimates. We'd rather say "we don't know" than make up a number.
Enterprise customers can deploy on a private cloud or on-prem with reduced model footprints. Some capabilities — particularly the largest models — require connectivity. We will tell you which features lose fidelity in an air-gapped setup before you commit.
It replaces the parts of analyst work that should never have been there — reformatting tables, looking up the same benchmark three ways, redrafting the same memo. It does not replace judgment, client conversations, or the discipline of being wrong in front of a steering committee.
Three layers. Retrieval is grounded in your documents and our benchmark database. Every numerical claim is cited; if it cannot be cited, the tool says "estimated" and shows its working. And the output is structured — when an answer doesn't fit a known frame, the tool surfaces that, instead of inventing certainty.
We'll set up a workspace for one of your active questions. If it doesn't earn its place, you walk away.