Your company is spending millions maintaining custom code, legacy integrations, and manual workflows that AI has made obsolete. We rip that out and replace it with AI-native systems that cost less to run and do more — across every department that touches revenue.
The AI demo worked. Leadership is excited. But nothing is in production — because nobody planned for the hard parts: permissions, data architecture, legacy system integration, cost control, and the organizational friction that kills most AI initiatives.
Meanwhile, your teams are still drowning in manual handoffs, spreadsheet workflows, and custom code that costs a fortune to maintain. Every department runs its own tools. Nothing talks to anything else. The people who built the integrations left two years ago.
We fix this. Not with a strategy deck — with architecture, code, and shipped systems.
Four things. We go deep on each — not a page from a consulting playbook.
Your AI project stalled after the demo. We've seen it a hundred times — security review killed the timeline, nobody designed the data architecture, costs spiraled, and the team is stuck between "prototype" and "production." We come in, diagnose the actual blockers, and build through them. Architecture, permissions, cost models, and working code — not another roadmap.
That billing system held together with duct tape. The CRM integrations that break every quarter. The finance spreadsheets that three people update every Monday. We identify the workflows costing you the most in maintenance hours and error rates, then replace them with AI-native automation. The goal is fewer systems, less custom code, and teams spending time on decisions instead of data entry.
AI agents that actually work in enterprise environments — with proper authorization (agents only access data the invoking user can see), structured memory that compounds over time, retrieval pipelines that stay within token budgets, and guardrails that keep costs predictable. We've built these systems from scratch and know exactly where they break at scale.
AI is only as smart as the data you feed it. If your departments run siloed systems with different naming conventions, update cadences, and data formats, no AI layer will produce reliable answers. We build the unified data platform underneath — pipelines, warehouses, semantic models — so your AI systems have something trustworthy to reason over.
Not concepts. Not decks. Systems in production.
A global data center company running on manual spreadsheets across departments and geographies. Data silos everywhere. A legacy Power BI implementation built by a vendor with brute-force ingestion — every change took weeks. Customer requests were delayed because nobody could get a unified view of capacity, contracts, or operations.
Ripped out the manual processes and built a unified Azure data platform — real-time and batch pipelines feeding Data Lakes, Fabric, and Data Warehouses. Standardized hierarchies, taxonomies, and tracking systems across every department. Deployed AI copilots so operators and executives could query operational data in natural language instead of waiting for report cycles.
Build an AI assistant that gets smarter per-user over time — not a stateless chatbot that treats every conversation as a blank slate. The system needed to remember context across sessions, retrieve relevant information from previously ingested content, and keep API costs under control.
Three-tier memory architecture: sliding conversation window, structured cross-session memory with category-aware compression, and a permanent fact archive with semantic retrieval. Token-budgeted context assembly at ~8K tokens per request. Async fact extraction pipeline that turns unstructured conversations into searchable structured data.
We're not a 50-person consultancy that assigns you a junior analyst.
Kognate is led by Industry Leaders with 20+ years building enterprise platforms and AI products. When you hire us, you get people who've done it, not people who read about it.
At Microsoft, we led Azure Data & AI platform strategy, working directly with Fortune 500 customers on $500M–$1B cloud deals — navigating the security reviews, compliance requirements, and organizational friction that most AI consultants have never seen up close.
As a founder, we built three products from zero to production — including an enterprise AI authorization control plane for agentic workflows, a usage-based billing platform processing millions of events per customer, and a consumer AI product with persistent structured memory shipped in weeks.
The difference: most consultants have either enterprise experience or startup building experience. We have both. We know what it takes to ship fast AND what it takes to survive enterprise security review. We write architecture docs, PRDs, code, and prompts — not just slide decks.
No six-month discovery phase. No hundred-page strategy doc. We diagnose fast, build fast, and iterate in production.
Two-week diagnostic. We audit your current AI efforts, data architecture, and legacy systems. We identify what's actually blocking production — it's usually not what you think. You get a concrete plan with architecture, cost projections, and a prioritized build sequence.
We embed with your engineering team and build the system together. Architecture, code, prompts, data pipelines, security review preparation — hands on keyboard, not hands on slides. Every week ends with something working that wasn't working before.
We're not trying to become your permanent vendor. The goal is to build the system, transfer the knowledge, and leave your team capable of running and evolving it without us. We document everything, train your people, and step back when the system is stable.
30 minutes, no pitch — just problem-solving. Tell us what's stuck and we'll tell you honestly whether we're the right fit.