Named capability case study: SIMARA AI
Building an AI-Native Consultancy
How we keep research, client work, delivery and learning connected—while people remain responsible for judgement and relationships.
By Lana Korzhuk, Founder & CEOPublished 26 August 2026
Discuss an AI Operating ModelAt a glance
We built the same discipline into our own business
Running a small consultancy means switching constantly between research, client conversations, proposals, delivery, and follow-up. We built our own way of working so useful context travels with the job instead of living in one person's head. AI prepares repeatable work; people still decide what matters, what is true, and what goes to a client.
The challenge
Enterprise expectations, without enterprise overhead
An expert-led consultancy has to learn quickly, respond consistently and deliver reliable work. Yet the knowledge needed to do that is often scattered across conversations, documents, tools and individual memory. Adding more software can create more fragmentation rather than more capacity.
The real question was not, “How do we automate more tasks?” It was, “How do we make the whole business more coherent while protecting the judgement clients are paying for?”
The approach
One connected system around the client journey
Connect signals to decisions
Relevant market, customer and operational information is organised into useful context instead of becoming another dashboard nobody acts on.
Encode standards, not just prompts
Quality criteria, evidence requirements and escalation rules are built into the workflow so good work does not depend on remembering the perfect instruction.
Keep people at consequential moments
AI prepares and supports. Human judgement remains explicit wherever a decision affects a client, a promise, a public claim or the direction of the business.
Turn outcomes into learning
What happened after a decision feeds back into the operating model, allowing the business to improve without repeatedly starting from scratch.
Business impact
A stronger operating model, not a louder machine
A more consistent client experience
Research, decisions and delivery standards stay connected, so each engagement benefits from what the business has already learned.
Expertise that compounds
Useful knowledge is captured as reusable operating intelligence instead of disappearing into inboxes, documents or individual memory.
Greater capacity without operational sprawl
Repeatable work is supported by AI while high-value attention stays focused on diagnosis, judgement and client outcomes.
Control that scales with the system
Clear ownership, review points and verification make the operating model easier to trust as it becomes more capable.
What this demonstrates
We operate the principles we recommend
The value is not an “AI company that automates its blog.” It is a consultancy designed to combine the speed of AI with clear ownership, evidence and human judgement. That same pattern can be applied through our workflow automation services to quoting, document handling, research, service delivery or reporting—always shaped around the real workflow and the risk of getting it wrong.
Frequently Asked Questions
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