TechStrat is a technology-focused M&A advisory firm that has advised on transactions across software, semiconductors, and internet infrastructure — including cross-border deals with acquirers in the US, Europe, and Asia. Its edge has always been sector fluency rather than scale: a small senior team competing against banks that field much larger deal teams.
Their operating thesis is simple: in technology M&A, the advisor who understands the target most deeply and moves fastest earns the mandate. The challenge was matching a larger bank's throughput with a fraction of the people.
The challenge
Competitive processes are won in the pitch. A larger bank can assign several analysts to work through a data room, build the comps, and map the buyer universe over a week. TechStrat could match the quality of that work — its partners are former operators and repeat sell-side advisors — but not always the speed.
The firm tracked the cost of that gap: on genuinely competitive mandates, its win rate sat around 34%, with losses clustering on processes where a larger bank simply arrived with more prepared material. TechStrat needed to walk into more pitches already knowing the business cold.
What changed
TechStrat made Deliverables AI the force multiplier that lets a small team prepare like a larger one.
1. Faster read on the data room
On a recent semiconductor carve-out (Project Meridian), the seller opened a data room with a couple of thousand files. Deep Research ingested the room alongside the public record and produced a cited synthesis of the business, its value drivers, and its risk profile in a few days rather than the better part of a week — giving the team a real head start on framing the story.
2. Institutional-grade comps and a clean data pack
Comps Analysis built the trading and transaction comps — operating metrics, EV/Revenue and EV/EBITDA multiples, and quartile statistics benchmarking the target against a ~20-company peer set (up from the 8–10 the team could assemble by hand under time pressure). Datapack Builder normalized three years of inconsistently-formatted financials into a clean, IC-ready workbook with documented assumptions.
3. A prioritized buyer universe
Buyer List produced a prioritized universe of roughly 40 strategic and financial acquirers, split by fit and rationale — including a few non-obvious Asian strategics surfaced during the research pass that the client hadn't considered.
4. A pitch that lands the point of view
Pitch assembled the thesis — valuation perspective, strategic alternatives, buyer landscape, and process timeline — into a polished, client-ready deck in TechStrat's house style.
By the numbers
| Metric | Before | After |
|---|---|---|
| Time for an initial data-room review | ~1 week | a few days |
| Peer companies benchmarked per pitch | 8–10 | 20+ |
| Buyers identified and prioritized | ~30 | ~40 |
| Competitive-pitch win rate | 34% | 42% |
The result
TechStrat now shows up to more competitive processes better prepared, and earlier — deeper analysis, a cleaner data pack, and a buyer map the client often hasn't seen elsewhere. Its competitive-pitch win rate improved from about 34% to 42%, and on Project Meridian the team was shortlisted and then mandated over a larger bank largely because it walked in already understanding the value drivers.
For a lean firm, that is the whole strategy: not to out-hire the competition, but to out-prepare it — and Deliverables AI is what makes the math work.