Kalala is an AI-enablement firm with a deeper endgame. We make companies dramatically more efficient with AI, and that work earns us the right — and the insight — to acquire the best of them, operationalize them with AI, and sell them for a profit.
The world is being rewired by AI, and most businesses don't know where to start. We meet them with something real — not a deck, an asset. We rebuild how a company runs on a foundation of well-organized data and AI agents, and we do it faster and cheaper than anyone they've talked to. That earns trust, generates revenue, and gives us an inside read on which businesses are worth owning. From there we take equity, transform them, and exit.
A repeatable, AI-run consulting practice that delivers real outcomes on a retainer — our revenue base and our proof of capability.
We increasingly choose clients we'd want to own — so every engagement doubles as live sourcing and diligence for acquisition.
Earn the right, take equity, AI-operationalize the business, and exit for a multiple of the value we create.
The traditional model is breaking in real time. Companies that never hired technologists are hiring engineers; owners are drowning in AI hype and noise. What they want is someone who understands what they're actually feeling and hands them something real, fast — not a six-month hypothetical.
Traditional firms grow only by hiring. You build the hour, you bill the hour. Speed and margin are capped by how many people you can put on it.
When everyone has AI, generic expertise is cheap. A company's only durable edge is its own well-organized, AI-ready data — and almost no one has it.
Owners know it and feel behind. They need a guide with enterprise depth who can move at boutique speed — and prove it in weeks.
The same AI that lets us run lean lets us take undervalued, inefficient businesses and make them dramatically more valuable — then sell.
We're not an ISV selling a tool nobody implements, and we're not a deck-shop selling slides. We bring 25+ years of enterprise experience in CRM, M&A, operations and AI — and we leave the client with a working AI "brain" for their business, not a PowerPoint. The first thing we build, every time, is the data foundation, because organized data is what makes everything after it fast, cheap, and theirs to keep.
We organize the company's data for AI first — the library before the speed-reader. It's the unlock for cost, speed, and their durable edge.
A real, working prototype — not requirements documents. We get something in their hands and start iterating immediately.
We refine against real use, and proactively keep the client informed — weekly updates, no standups, no gating.
We harden it for production data and operationalize it — the engagement compounds month over month on a 12-month retainer.
$10K / month retainer, 12-month commitment, cancel anytime. Under-promise, over-deliver, so clients stay by choice.
A $5K Blueprint — priced to weed out the un-serious and to let us get in and prove value fast before a retainer.
Our best case study is us. We run Kalala itself on the exact infrastructure we sell — the firm is the demo.
The work of a consulting firm is mostly repeatable knowledge work, so we turned each job into an AI agent, organized like staff: managers who coordinate, specialists who execute, and two people who own judgment, relationships, and the final yes. We don't wait on the one expert. We're not limited by the roster. Agents carry the load; people hit the checkpoints.
The reason this isn't ruinously expensive to run is the same reason it's valuable to clients: data architecture. AI is a speed-reader; your data is the library. Most companies leave the books on the floor, so the AI burns time and money figuring out where things are. We build the Dewey Decimal system — so even complex work runs on small, cheap models instead of the priciest ones. That keeps our compute low and gives the client the one thing that's actually defensible in an AI world: their own AI-ready data.
Well-organized data lets us run complex tasks on models a fraction of the cost of the frontier ones. Efficiency is engineered, not assumed.
No waiting on the one expert, no roster limits, no people-speed ceiling. The traditional constraints simply don't exist.
Lessons and playbooks feed back into the system, so each project is faster and cheaper than the last. Knowledge doesn't walk out the door.
Today's numbers are real; the scale case is illustrative but conservative. The point: capacity grows with clients while the human cost base barely moves — services revenue at software-like margins.
Illustrative. Founder loaded at a $500K market placeholder though actual draw is ~$0 today, so near-term cash generation is higher. Operating margin ≈ 46% (≈48%+ on a normalized basis). Overhead derived from 12 months of actuals, ex-personal and ex-owner-discretionary; an investor-grade model should have these confirmed by our accountant.
On pricing: $10K/month is almost certainly under-priced for the value delivered. We're holding it on purpose until the first engagements are complete, so any increase is driven by data and client feedback rather than a guess — and raising it is a deliberate lever we want a partner's eyes on. There's also a Salesforce lesson here: their product carried ~90% gross margin on compute, then eroded it with bloated SG&A and human inefficiency. A services business doesn't have to repeat that — which is exactly the structural advantage we're building.
Each stream funds and feeds the next. Enablement pays the bills and sources targets; systems integration adds predictable revenue; value creation is where the real upside lives.
The core practice: $10K/month retainers delivering real outcomes. Predictable revenue, proof of capability, and the funnel that surfaces acquisition candidates. Increasingly run by a dedicated services lead so the partners can focus upstream.
Becoming an implementation partner for established platforms (e.g., UiPath) — a separate, more predictable revenue line that shares our contracting and billing engine and broadens the funnel.
Convert the right engagements into ownership: take equity or value-based deals, AI-operationalize the business, and share in (or capture) the upside on exit. This is where the outsized returns are.
We don't want to be a client-services business forever — that's the Trojan horse and the foundation. The destination is value creation: using our ability to make a business dramatically more efficient as the lever to own a piece of the outcome. We have two deliberate ways in.
We deliver as a strategic partner, prove exponential value, then propose a value-based deal — "stop paying us; give us a share of the upside we create." The most natural path, and the one our funnel produces.
When we spot an attractive, ownable business, we come in openly as a disruptor-acquirer with an explicit thesis and a stake — increasingly a differentiator in its own right as our track record grows.
Either way, the model is the same: get a business AI-enabled and acquisition-ready in a defined window, then sell it for a multiple of the value we created — the A&M-style model where the operators share directly in the deals they drive.
Target deal sizes, equity stakes, hold periods, and return targets are intentionally left open — they require genuine private-equity experience to set responsibly. Quantifying and structuring this is precisely the role we're looking to a value-creation partner to own. The gap is the ask, not an oversight.
The enablement practice keeps a wide top of funnel; from it we apply a tightening filter toward businesses we'd actually want to buy and sell.
Inbound and referral demand for AI enablement keeps the funnel full and the brand visible. Empathy plus something real cuts through the AI noise.
We increasingly prioritize clients with strong acquire-and-sell potential — so delivery doubles as diligence. Not everyone who can pay the retainer is the right fit.
Packaged outcomes — "by month six, here's the operational lift" — make the value legible to owners and to future buyers alike.
We keep the core deliberately tiny and hire only when capacity is genuinely exceeded. Agents absorb the labor; partners bring deal-making, capital, and reach. Committed roles are named; the rest we're actively building.
Growth, client relationships, and strategy — and the architect of the Kalala "brain," automating the firm's own delivery so the model scales with agents, not headcount.
Delivery and build — bespoke AI generation with a team of agents, plus the systems-integration practice. The technical engine of the firm.
Senior consulting/PE leadership to own the acquisition thesis, deal structures, and value-based economics — the part of the plan we're intentionally not modeling alone.
A trusted operator to own the day-to-day client-services business at scale, freeing the partners to focus on value creation and deals.
Angel capital and a private-equity network prepared to fund the vehicle as the acquisition strategy takes shape.
The agent operating system that does the labor a consulting team would — scaled on demand, governed by the two-person core.
Bank the reps now; earn the right to acquire as the system and the track record mature.
Used carelessly, AI spend can exceed the cost of people.
Mitigation: data architecture first, so complex work runs on small, cheap models; bring-your-own-LLM and open-source where it fits.
Owners are overwhelmed by AI hype and skeptical of vendors.
Mitigation: lead with empathy and a real, working prototype in three weeks — not hypotheticals.
Client services alone can become a low-leverage treadmill.
Mitigation: automate the low-value work, stay selective, and treat enablement as the on-ramp to ownership, not the destination.
Acquisition timing is lumpy and outside our control.
Mitigation: a predictable retainer + SI revenue base underneath the episodic deal upside.
A tiny core concentrates knowledge and risk.
Mitigation: the system itself captures institutional knowledge; partners and agents distribute the load.
$10K/month may be mispriced until proven.
Mitigation: hold price, complete engagements, then raise on real data and feedback.
Kalala is a real, cash-positive business today and a vehicle for something much larger tomorrow. We're looking for the right people to build it with.
Bring deal-making and PE acumen; own the value-creation engine and share directly in the upside.
Back a lean, high-margin services business with a clear path to an acquisition-and-exit strategy.
Join a tiny, high-leverage core where agents do the grind and people do the work that matters.