SimpleDocs on r/legaltech

I really like our competitors that are in this AMA. I've told them each as much over the last couple of years.

Scott is wicked smart, and fearless in terms of testing market demand for new features.

Min-Kyu is building a killer enterprise brand. We look up to Ivo in so many ways.

Ross is a total stud and executes like crazy. We lost a HUGE deal to them last year, and I reached out to congratulate him, and he didn't even know they'd won the deal yet. I respect the focus!

We just completed a study with 270+ lawyers who reported using a range of general AI tools as their primary and compared that survey data against our own customers. There's clearly massive adoption of general AI tools in transactional work. But there's also huge distance between how our customers feels about SimpleDocs v Claude and others.

We win by a large margin on trust (verifiability) of out output and frequency of use.

There will be some healthy convergence here. For enterprise customers, we see a greater need for purpose-built tools that allow for advanced configurations and controls. Our Legal Engineering team will tell you that large corporate in-house teams need the support we provide in getting tools like ours in place across large teams.

We're transparent on pricing (see pricing page). And as our products and features expand, our prices go up. We're not VC backed, so I think our increases are aligned to value rather than external investor pressure.

We think about these metrics at three levels. AI quality. We built a full eval system that covers every workflow: clause analysis, redlining, drafting, chat, repository queries, document summaries. Each one has scored criteria with pass thresholds, and every prompt version has to clear its suite before it hits production. We also stamp every AI output with the prompt version that generated it. So when a user flags something, we trace it to the exact prompt, run the evals, iterate, and ship. Customer analytics. Every customer gets a real-time dashboard with documents reviewed, team adoption, top playbooks, speed-to-sign, time allocation between sides, and negotiation cycles. Filterable by date range with period comparison built in. They can see the ROI without asking us for a deck. Product usage. We track aggregated, anonymized usage patterns across the product. Requests by purpose, feature adoption, growth trends. This tells us what’s getting used and where to build next.

We win and lose against each of these competitors every day. It's very much a converging market. That said, when we win, this is why:

-Market data: before SimpleDocs I spent \~15 years building the Law Insider database. Sitting on top of that data is a huge differentiator with our customers.

-Platform: we didn't start as a Word Add-In. The initial thesis was an unbundled CLM (I know that's a bad word now) so we've invested in an AI native repository and documentation automation.

-Pricing: we're typically 25-50% less expensive.

I wish people understood that this isn't a winner take all market. It's true that not all Legal AI vendors will survive, but more will reach $10M and $100M ARR than you probably think. This market is massive!

You totally should!

Change management!

These guys drive the market demand for us. It creates category awareness. For every lawyer that wants to build skills on Claude, there are 10 who want to buy a trusted Legal AI specific solution. There's more accountability, there's more purpose-built features.

We're staying in our lane with contracts. But this goes way beyond just contract review. There's a reimagining of CLM happening here, and I think the vendors who can win in the contract review category will naturally get pulled into AI Repository, Workflow Automation, etc.

They'll continue to cut out the bottom of the market. That's to be expected. What's less clear is whether LegalQuants (shout out Jamie Tso) will become the new Legal Ops leaders in corporate in-house teams and start cutting into the enterprise opportunity for vendors like SimpleDocs. Time will tell. I like our position. I respond to customer issues at 2AM on Sundays.

ICP = large corporate in-house legal teams.

We mostly sell into legal departments who today own their own Legal AI budget. IT and Ops are typically involved in the evaluation but it's not their budget or buying decision. This may change. We'll see.

We are growing extremely fast, so market demand is certainly one strong signal. Our customers are very aware of the general AI tools and they choose us.

We also recently completed a pretty comprehensive market study (\~270 respondents) and the results showed wide adoption of the general tools by transactional lawyers, but with far less confidence in the output when compared to Legal AI tools like SimpleDocs. This also resulted in significantly less usage.

We use leading models from OpenAI, Anthropic, and Google. Our philosophy here is that no single model is best at everything, so we evaluate foundation models on a per-use-case basis. Our engineering team maintains a prompt management and eval system that lets us routinely test model performance against specific tasks. That gives us the ability to keep refining without introducing regressions.

Native Law Insider integration.

Our ICP is corporate in-house rather than law firms (tho we serve a few thousand solos and small firms as well). Internal champions and power users tend to very. But the general buying thesis for corporate in-house is that a tool like SimpleDocs will streamline the higher volume work often owned by more junior team members.

Strong agree with the other comments here. Time to value for a Microsoft Word Add-In is so obviously different for our customers and prospects that there's very little CLM battle scars brought into the evaluation process. They see it, they test it, they buy it. We implement fast.

Playbooks, precedent, training. Those tend to drive the implementation process. Depending on level of complexity and scope of playbooks and precedent, implementation can take a week or a month. But typically not longer. Training is ongoing, and we teach you and the team how to build and update playbooks and precedent over time, so you're less dependent on our Legal Engineering team.

Having AI generated output that anchors back to verifiable context is where trust will come from. That can be in the form of internal policies (playbooks), historical precedent, and external market data (like Law Insider). But always with the ability to audit/verify the recommendations. No black box recommendations!

We are a software company that leverages LLMs to deliver value to corporate in-house teams. Some of that value is massively reliant on LLMs, and other parts of it not at all. We're not building our own AI models, but we are building software and data / context layers to meet the needs of our customers.

We have contractual terms in place with our customers that deal with our obligations and place a burden on us to maintain confidentiality to the highest degree. We work with many very large in-house teams in high risk, regulated industries and our terms cover their requirements. In terms of a regulator auditing you, I assume you mean a data protection regulator. We have a robust DPA that has been deemed sufficient by our existing customers and that creates obligations on us to only act on your instructions and comply with all relevant DP rules that apply to us.