We open with honest qualifying.
The partner fit call is genuinely diagnostic. About 30% of fit calls end in a recommendation that we are not the right answer.
Build It, Buy It, or Bring In a Partner - A Mid-Market CTO's AI Decision
Three honest answers to "how should we implement AI?" - and where Logiciel actually fits among them. (Spoiler: not every time. Most of the time.)
Build is the right answer when AI is going to be a multi-year, multi-program capability that is core to your competitive position - and when you can credibly hire the people to staff it within your geography and budget.
Build wins when:
Build's honest downsides:
If "build" is the right answer for your context, we will tell you on the call. Several of our long-term clients started with us as a partner, built an internal team alongside us, and graduated to a smaller partnership footprint over time. That progression is healthy, not a defeat.
Buy is the right answer when the AI capability you need is generic enough that a commercial product solves it, and when integration depth doesn't matter much for your competitive position.
Buy wins when:
Buy's honest downsides:
If "buy" is the right answer for a specific capability in your portfolio, we will tell you on the call. Most mid-market AI portfolios are a mix of buy and partner - that's normal and healthy.
Outputs:
The AI capability is differentiated - it touches your proprietary data, your unique workflow, or your competitive position.
You need production-grade output in 8–16 weeks, not 6–9 months.
You want to validate the AI investment before committing to permanent headcount.
Your existing engineering team is capable but stretched, and AI is not their primary muscle yet.
You operate in a regulated environment where governance and compliance posture matter from day one.
Outputs:
Ongoing partner spend is real. It needs to be justified against the alternative of internal hiring at your scale.
Knowledge transfer is required. A bad partner doesn't transfer; a good partner designs for it from week one.
Misaligned incentives are possible. Partners can underdeliver into long contracts. The structural fix is short, milestone-bounded engagements (which is how we structure ours).
The AI consulting and implementation market is built around two ends - startup speed at one end, Fortune 1000 scope at the other. Mid-market companies fit neither shape.
move fast but are typically light on governance, compliance, and enterprise integration depth. That's a problem when you operate under SOC 2, HIPAA, GLBA, or sector-specific requirements.
carry the right governance and compliance muscle but come with partner-pyramid pricing, 6–12 month procurement cycles, and engagement minimums that don't fit a mid-market budget.
Mid-market AI implementation needs a third shape: enterprise-grade governance and engineering discipline, delivered at mid-market velocity and pricing. That's what Logiciel's mid-market practice is structured around.
The partner fit call is genuinely diagnostic. About 30% of fit calls end in a recommendation that we are not the right answer.
Our engagement minimums, pricing, and velocity are designed for organizations between 200 and 2,000 employees. We are not a re-skinned enterprise consulting offering.
Documentation, onboarding playbooks, and explicit transfer milestones are part of every engagement. We expect that some of our best mid-market relationships will reduce partner footprint over time as your internal team grows.
SOC 2, HIPAA, GLBA, and sector-specific posture are not an upcharge - they're how we deliver. This is the gap that startup-shop partners can't credibly fill.
Every engagement has a defined business-outcome metric (workflow time saved, accuracy lift, cost reduction, revenue lift) and we report against it.
An AI implementation partner is an external engineering organization that builds and operates AI capabilities on your behalf - typically inside your environment, under your governance, and integrated with your existing systems. The partner is distinct from a strategy consultancy (which produces decks but doesn't build), a point-solution vendor (which sells a product), and an internal team (which is permanent headcount). Most mid-market companies engage partners for AI capabilities that need customization but don't justify a permanent internal AI engineering org yet.
The clearest signal is the timeline. If you need production AI in 8–16 weeks, a partner is materially faster than internal hiring. If you need it in 9+ months and AI will be a permanent core capability, internal hiring is the better long-term economics. Most mid-market companies start with a partner, validate the investment, and then layer in internal hiring once the value is proven.
Logiciel's mid-market practice is structured for organizations of approximately 200–2,000 employees and $50M–$500M annual revenue. We also work with smaller startups (separately, with a different engagement model) and enterprises (separately, through our enterprise AI consulting practice). The mid-market practice is intentionally sized for this segment - not a re-skinned enterprise offering.
Logiciel's mid-market AI implementation work has the deepest patterns in SaaS, FinTech, PropTech, ConstructionTech, healthcare, insurance, and B2B platforms. We have working patterns in most other industries; we will tell you on the fit call if your specific industry has constraints we don't have direct experience with.
Engagements range from low-six-figure fixed-scope sprints to monthly retainers in the mid-five to low-six figures for dedicated squads. The numbers are materially below Big Four AI consulting pricing for equivalent scope because we don't carry the partner-pyramid cost structure. We provide indicative pricing on the fit call.
Three structural choices. First, every engagement has a defined milestone shape - no open-ended retainers. Second, documentation and knowledge transfer are explicit deliverables. Third, the IP, code, and operational artifacts are yours from the first commit. We design for the relationship to be valuable, not captive.
The fit call ends with a recommendation. About 30% of fit calls conclude that we are not the right answer - usually because the right answer is internal hiring (for very large AI bets), a point-solution vendor (for commodity capabilities), or another partner with a specific industry depth we don't have. We make those recommendations directly and, where useful, refer to specific alternatives.
The fit call is genuinely diagnostic. You'll leave with a written recommendation: build, buy, partner, or a hybrid. If partner is the right answer and Logiciel is the right partner, we'll scope from there. If not, you'll have a clearer internal decision to take to your CEO.