Build vs. Buy: What I’ve Learned Building Platforms
Key takeaways
- Feasibility isn’t the question. You can build this. It’s software, and a capable team can do it. There are questions to consider around opportunity costs and regulatory exposure.
- The hard part isn’t any single layer. Capture, archive, search, AI models, and workflows each look manageable alone, but the sum is a program that keeps growing.
- Regulatory defensibility is the difference between this and internal search. Retention, supervision evidence and audit trails must hold up in an exam.
- Purpose-built models can deliver the accuracy you need at a lower cost than a large general-purpose model. A tiered design keeps speed and cost in check at volume.
Let me start with the obvious
If you’re a CTO or senior engineer reading this, you already know you can build a communications intelligence platform. You have the talent, and the tools have never been better. If I were in your seat, I’d be asking the same question: can we build this, and should we?
Many teams start with a reasonable stack: open-source embeddings, a vector database and cloud object storage. That’s a good start, and it covers perhaps a fraction of the problem. Here is the rest to consider, in six pieces.
1. Capture is a moving target
Capturing communications sounds like a connector problem. It is no longer a simple task of connecting in email and being done. In practice, channels change constantly. Vendors update APIs, new collaboration tools appear, and formats shift. Each one needs to be built, tested, and kept current.
The real cost isn’t the first integration. It’s maintaining them indefinitely, and adding new ones as your business adopts new ways to communicate. It is important that you plan for this with the appropriate teams, strategy and technologies.
Completeness is its own build. A missed channel or a silent connector failure isn’t a bug ticket. It’s a compliance gap. You need monitoring and reconciliation to prove that what was supposed to be captured, actually was.
2. Archiving is easy, until you need to put the data to work
Storing data to meet retention requirements is a well-understood problem. Preserving context, securing and protecting the integrity of data is critical.
The complexity starts when people need to find things. Keyword search only goes so far. If you want to use AI, you need semantic search, and that means much more:
- Metadata and tagging: a consistent way to enrich every record as it lands.
- Embeddings and RAG: allowing AI systems to retrieve relevant context from your archive.
- Semantic graphs: to analyze relationships between people, topics, and events, not just individual messages.
And if you want surveillance on top, that adds another layer of requirements.
Storage itself is also bigger than “buy disks.” A multitiered storage design brings its own planning, tooling, and cost model. The real expense is hardware and software, plus the people to run it, and the pricing is easy to underestimate at the outset.
3. You need an engine, and the obvious approach is the expensive one
The third piece is the intelligence layer: machine learning and language models. We started where many teams do, with general-purpose LLMs. They’re great at a wide range of data tasks, but communications surveillance isn’t a general task, and broad capability doesn’t automatically translate into the precision this work demands.
What worked for us was fine-tuned, purpose-built models. We got the accuracy we needed without paying to run a big general-purpose LLM across every message. We also tier them. Smaller, purpose-built models handle the bulk of the volume, and only the harder cases get escalated to a larger model. That keeps latency and cost down at scale.
Picking the model is just the first step. The tuning is a balancing act: cast the net too wide and your reviewers drown in false positives and alert fatigue; narrow it too far and you create blind spots, potentially missing the one message that matters. Getting that balance right takes many iterations and two kinds of expertise working together: deep technical knowledge and an understanding of the regulations and how regulated firms actually communicate.
Then there’s the long tail of keeping a model in production. You must watch for drift as language and behavior evolve and make outputs explainable so compliance teams can trust the system and defend their decisions to regulators. That goes beyond accuracy. Regulators and internal audit will also want documented false-positive tuning and a record of how and when the models were validated, and someone has to produce and maintain all of that.
My advice: don’t chase the most powerful model in the abstract. Build an intelligence layer that’s precise, explainable, and affordable for the problem you actually have.
4. Regulatory defensibility is the piece that’s easy to miss
This is what separates a communications intelligence platform from internal search, and it’s where the liability sits.
In an exam, a regulator may ask:
- Retention and immutability: Can you show records were retained for the required period and weren’t altered?
- Supervision evidence: Can you show who reviewed what, when, and what was escalated?
- Audit trail: Can you reconstruct how the system reached its decisions, including model decisions?
- Capture completeness: Can you demonstrate nothing in scope was missed, and explain any gap?
- Explaining the system: Can you walk the examiner through how it works and where its limits are?
A purchased platform comes with a track record of delivering on these questions. A homegrown one starts from zero, and the person answering is you and your team.
Note this is not legal or regulatory advice, so validate it with your compliance team.
5. Workflows are where the work never ends
Once you’ve built your capture, archive, and models, next is workflows. Discovery, compliance reviews, and investigations each need their own flow, and each one drags in review queues, escalation paths, access controls, and audit trails. None of that is exciting to build, but it’s what your users actually leverage every day, and it’s never really finished. Every new use case or regulatory ask means another workflow, or a change to one you already shipped.
My advice: treat workflows as a product with its own roadmap, team and owner.
6. Upkeep is the part budgets forget about
Then there’s the upkeep. Models drift, channels change, regulations shift, and the whole stack needs constant patching and updates. The build isn’t a project you finish; it’s a product you run. That means a standing team, a real budget, and a roadmap that keeps going long after the launch celebration. Before you commit to building, ask who owns this in year three, and whether you’re willing to fund them for as long as the regulations stay in force.
If you decide to build
Here’s my advice from the inside:
- Scope all six pieces up front, not just the one your team is most excited about.
- Budget for maintenance, not just the initial build. It’s a standing cost.
- Plan for the model layer to change underneath you.
- Don’t treat storage as an afterthought. Tiering and cost modeling matter at scale.
- Treat regulatory defensibility as a requirement, not a later phase.
A simple decision test
- Build if compliance and communications intelligence is core to what you sell, and you can commit to staff and retain specialists for capture, models and regulatory evidence for the long term.
- Partner if your obligations exist today and you can’t accept phased coverage with gaps in between, or your engineers would add more value on products that differentiate you.
- Do both if you want to own what’s unique to you and not what’s a commodity. That’s the hybrid path.
If you’re weighing this decision, I’m happy to compare notes. Explore our developer portal, or reach out and let’s talk through your plan, whichever way you lean.
Frequently asked questions
Yes, but the effort is larger than most estimates. The surface area spans capture, archive, search, models, and workflows, and all of it needs ongoing maintenance.
They’re a sound foundation for search. They don’t provide capture across changing channels, retention and immutability controls, supervision workflows or an audit trail an examiner will accept.
Meeting retention requirements is the straightforward part. Semantic search requires metadata, embeddings, RAG and semantic graphs.
Typically about retention, supervision evidence, audit trails and capture completeness. See section 4. This isn’t legal or regulatory advice, validate with your compliance team.
People, ongoing maintenance, tiered storage, model validation and documentation, audit support and the cost of keeping channels and models current.
It’s a fair worry. Ask any vendor about data portability, open APIs, ability to bring your own models, and how you would export your archive and supervision records if you left tomorrow. You should be able to take your data with you in a usable format.
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- Build vs. Buy: What I’ve Learned Building Platforms - November 2, 2026
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