On this page
- Key Takeaways
- What Is a Custom AI App?
- Step 1: How Do You Choose the Right Use Case for a Custom AI App?
- Step 2: How Do You Scope a Custom AI App Before You Build It?
- Step 3: What Architecture Does a Custom AI App Need?
- Step 4: How Do You Build a Custom AI App in Short, Visible Cycles?
- Step 5: How Do You Launch and Hand Over a Custom AI App?
- How Do Strong Portfolio Teams Avoid Common Custom AI App Mistakes?
- Checklist for Evaluating Your Custom AI App Build
- Build Custom AI Apps for Your Portfolio with PressW
- FAQ
Key Takeaways
- Pick the use case before you pick the technology. The first step of a custom AI app build is choosing one workflow that moves the value creation plan and has data ready to support it.
- Scope on one page. Users, workflow, success criteria, baseline, and milestones should fit on a page your operating partner can sign.
- Start with the simplest architecture that works. Add retrieval, integrations, and autonomy only when the use case needs them.
- Build in short cycles with evals from the first sprint. Working software every cycle, graded against real examples, catches problems while they are cheap to fix.
- Launch means handover. A custom AI app is done when the portfolio company's team can run it, prove it works, and improve it without you.
Most portfolio companies have no shortage of AI ideas. The CEO saw a demo. The CFO wants invoice processing automated. Customer support wants a chatbot. Sales wants account research done for them. Each idea gets a pilot, a few get a proof of concept, and very few become software that people use every week.
The numbers bear that out. In a 2025 survey of more than 1,000 companies, S&P Global Market Intelligence found the average organization scrapped 46% of its AI proofs of concept before they reached production. Private equity is no exception: L.E.K. Consulting's PE Pulse 2026 found that many firms have proven AI's value, yet relatively few have scaled it across their portfolios.
Most stalled projects trace back to a decision made before anyone wrote code: a use case picked for excitement rather than value, a scope nobody wrote down, or a launch with no owner on the other side. This guide walks through how to build a custom AI app for a portfolio company in five steps, from choosing the use case to handing over a production system, with the tables and checks to use at each one.
What Is a Custom AI App?
A custom AI app is software built around one specific business workflow that uses an AI model to read, reason over, or generate the information that workflow depends on, and that runs inside the company's own systems, permissions, and review steps.
A general AI assistant answers whatever a user types. A custom AI app does one job for a defined set of users, pulls from the data sources that job needs, and puts its output where the team already works. Examples in a portfolio company include a quote intake tool that reads submissions and fills the pricing system, a contract review app that flags non-standard terms, or a support copilot that drafts answers from the product knowledge base.
What are the core components of a custom AI app?
Every custom AI app is built from the same handful of layers. The use case decides how much of each you need.
| Component | What it does | Portfolio company example |
|---|---|---|
| Model | Reads inputs and produces the output: a summary, an extraction, a draft, a classification | Claude reading an inbound insurance submission |
| Knowledge and retrieval | Finds the right documents or records for each request (retrieval-augmented generation) | Pulling the matching product guidelines before drafting a quote |
| Integrations | Connects to the systems where inputs live and outputs land | Reading from email and the CRM, writing to the ERP |
| Interface | Where people use the app | A review screen, a sidebar in an existing tool, or an API another system calls |
| Human review | Where a person checks, edits, or approves output | A queue where an underwriter approves each draft quote |
| Controls and observability | Access, logging, evals, and usage tracking | SSO, role-based access, audit logs, eval pass rates, cost per run |
Step 1: How Do You Choose the Right Use Case for a Custom AI App?
Use case selection is the step most teams rush and the one that decides whether everything after it pays off. The goal is one workflow, picked on evidence, that the whole team can explain in a sentence.
Start from the value creation plan
List the lines of the value creation plan first: margin expansion, cost to serve, revenue per rep, working capital. Then ask each function which repeated work sits underneath those lines. A use case that cannot be tied to a line in the plan is a hobby project, however impressive the demo.
Score every candidate the same way
Run each candidate through the same six questions. A use case that fails two or more is not ready to build.
| Criterion | Core question | Why it matters | Red flag |
|---|---|---|---|
| Value | Which line of the value creation plan does it move? | Ties the build to margin, revenue, or cost to serve | "Everyone else has a chatbot" |
| Frequency | How often does the work happen? | Repeated work compounds the return | A task done once a quarter |
| Data readiness | Does the data exist, and can the app reach it? | Missing or scattered data stalls more builds than model choice | The inputs live in one person's inbox |
| Output shape | Can someone show three finished examples? | Defines what "correct" looks like before you build | Nobody agrees what a good output is |
| Owner | Who will use it every week and judge it? | Adoption needs a named user on the portco team | The only sponsor sits at the fund |
| Cost of an error | What happens when the output is wrong? | Sets how much human review the app needs | Errors reach customers or regulators unreviewed |
Place the shortlist on a value and readiness matrix
Plot the survivors on two axes: value to the plan and readiness to build. High value and ready goes first. High value but not ready becomes a data or process project with its own timeline. Low value but easy usually belongs to an off-the-shelf tool or a configured assistant, and low value and not ready gets parked.
Tip: If the data question keeps coming up, start with an AI & Data Readiness Assessment, which ends with a scored baseline and a ranked list of opportunities.
Step 2: How Do You Scope a Custom AI App Before You Build It?
A good scope fits on one page and answers every question a skeptical operating partner would ask. Write it with the people who do the work today, not only the sponsor.
Say a portfolio company that brokers specialty insurance picks quote intake as its first use case. Brokers email submissions in a dozen formats, and an account manager retypes each one into the rating system before anyone can price it. The scope for that app might look like this (illustrative):
| Scope element | What to write down | Illustrative example |
|---|---|---|
| Users | Who uses the app and who approves its output | Account managers use it. Underwriters approve each quote. |
| Workflow | The steps today, in order, with the ones the app takes over | Read submission, extract fields, check appetite, fill the rating system |
| Success criteria | Specific, measurable targets for accuracy, speed, and adoption | Field extraction accuracy target, turnaround time target, share of submissions processed |
| Baseline | How the work performs today, measured before the build | Current hours per submission and current turnaround time |
| Data access | Every source the app reads and every system it writes to | Shared inbox and rating system, read and write. CRM, read only. |
| Out of scope | What the first release will not do | Binding coverage or sending anything to the broker |
| Milestones | Releases with a clear definition of done | First slice on real submissions, then rating system write-back, then launch |
Anthropic's guidance on defining success criteria recommends targets that are specific, measurable, achievable, and relevant to the application's purpose. Write them before the build starts, and measure the baseline with the same yardstick, or you will have nothing to compare against at launch.
Step 3: What Architecture Does a Custom AI App Need?
Pick the simplest stack that does the job inside the company's controls. Anthropic's own guidance on building effective agents recommends finding the simplest solution possible and adding complexity only when it improves results. That advice applies to every layer of a custom AI app.
| Layer | Decision to make | Where to start | When to add more |
|---|---|---|---|
| Model | Which model, and where does it run? | One capable model through an API in your cloud or a governed workspace | Volume or latency justifies routing simpler tasks to a smaller model |
| Knowledge | How does the app find the right facts? | Retrieval over a curated, approved document set | Users need live numbers from structured systems, so add direct queries |
| Integrations | Where do inputs come from and outputs go? | One source system and one destination | Users ask to remove the copy-and-paste steps that remain |
| Interface | Where do people use it? | Inside a tool they already open, or one simple review screen | Usage data shows a new surface would save real time |
| Human review | Where does a person check the output? | Review of every output at launch | Eval pass rates hold steady and errors are cheap to fix |
| Controls | Who can see and do what? | SSO, role-based access, and audit logging from day one | Never optional, so this layer does not wait |
Two architecture choices deserve extra care in a portfolio company. First, keep data access scoped by role so the app only reads what its users are allowed to see. Second, plan for the guardrails the use case needs, such as blocking certain outputs or requiring approval before anything leaves the company.
Step 4: How Do You Build a Custom AI App in Short, Visible Cycles?
Long builds hide problems. Short cycles surface them while they are cheap to fix. Aim for working software in front of real users early, then improve it every cycle.
Ship a thin slice first
The first release should run the whole workflow end to end on real inputs, even if it only handles the most common case. A quote intake app that handles one submission format from inbox to rating system teaches you more than one that handles every format but never writes anywhere.
Run evals every cycle
Turn the three or more finished examples from Step 1 into a test set, then grow it with real cases as you go. Anthropic's guide to building evaluations recommends test sets that mirror the real task mix, edge cases included. Grade every release against that set before anyone sees it.
Capture reviewer edits as feedback
Every time a reviewer corrects the app, that correction is training data for the next cycle. When PressW built the Marketing Analyzer for HighCamp Compliance, the review screen recorded each edit and deletion a compliance reviewer made to an AI-generated flag, so detection could improve over time. The app ran 14 specialized LLM chains in sequence to replicate an expert review, and the human stayed in the loop for the final call.
Run security review alongside the build
Security and compliance review should run alongside development, not wait for the end. A review that starts in the last week of a build tends to find the problems that push the launch back.
Tip: See how PressW runs scoped milestones, short build cycles, and handover on our Custom AI Application & Platform Development capability page.
Step 5: How Do You Launch and Hand Over a Custom AI App?
A pilot proves the app can do the job once. Launch means the portfolio company's team runs it every day, with access you can defend and quality you can prove. Before you call it live, check each of these.
| Launch requirement | Core question | Why it matters for a portfolio company | Risk if skipped |
|---|---|---|---|
| Eval suite passing | Does it meet the success criteria from Step 2? | The board will ask whether it works | Quality is a matter of opinion |
| SSO and role-based access | Who can see and do what? | Portco data often includes customer and employee records | Users see data they should not |
| Load test | Does it hold up at real volume? | Month-end and peak season are when it matters most | It slows or fails on the busiest day |
| Audit logging | Can you show what it did and why? | Auditors, customers, and acquirers ask | No record when an output is challenged |
| User documentation and training | Do users know how and when to use it? | Adoption decides the return | The app launches and nobody changes how they work |
| Runbooks and a named owner | Who fixes it, tunes it, and decides what is next? | Workflows, data, and models all change | It drifts and quietly stops being used |
Hand over the code and the knowledge
The portfolio company should own the source code, prompts, eval set, and documentation, all in repositories it controls. That protects the investment through a change of partner, a management change, or an exit, when a buyer's diligence team will ask who can maintain the system.
Plan the next release from usage
Track usage, eval pass rates, and cost per run from the first week. The next release should come from what users do and ask for, not from the original wish list.
How Do Strong Portfolio Teams Avoid Common Custom AI App Mistakes?
Teams that get custom AI apps into weekly use pick narrow, measure early, and name an owner. The ones that stall usually hit one of these.
Common pitfalls
- Choosing the use case by demo. The most impressive demo is rarely the workflow with the most value or the best data. Score candidates on the six criteria before anyone builds.
- Skipping the baseline. Without a measurement of how the work performs today, nobody can show the app made a difference.
- Starting with the hardest architecture. Multi-agent systems and fine-tuned models have their place, but most first apps need one model, good retrieval, and one integration.
- Building in isolation. If real users first see the app at launch, the team finds out too late that it solves the wrong part of the workflow.
- Treating security review as a final gate. Late reviews find late problems. Run them alongside the build.
- Launching without an owner. An app nobody owns drifts out of step with the workflow within months.
Checklist for Evaluating Your Custom AI App Build
Use this before you approve the budget, whether the portfolio company's team is building the app or a partner is.
- The use case maps to a named line of the value creation plan
- It passed the six scoring criteria: value, frequency, data readiness, output shape, owner, and cost of an error
- At least three real examples of the finished output are on file
- A one-page scope covers users, workflow, success criteria, baseline, data access, out of scope, and milestones
- The baseline was measured before the build started
- The architecture starts simple and each added layer has a stated reason
- An eval set exists and every release is graded against it
- SSO, role-based access, and audit logging are in the first release
- Code, prompts, eval set, and documentation live in repositories the company controls
- A named owner and a review cadence are in place for after launch
Build Custom AI Apps for Your Portfolio with PressW
A custom AI app pays off when it starts from a use case tied to the value creation plan, ships on a one-page scope, runs on the simplest architecture that works, gets graded every cycle, and lands with a team that owns it. Get those right and the model choice is the easy part.
PressW is an Official Anthropic Partner that designs, builds, and launches custom AI applications and platforms, from internal tools and copilots to customer-facing features, delivered with documentation, evals, and training. We took a healthcare startup from concept to a deployed production AI platform in 6 weeks, and we built ClinicalNet a clinical trial search engine that searches 65,000+ active trials and returns matches in under 5 seconds. See how we work with private equity firms and their portfolio companies.
If you have a shortlist of use cases, we will score it with you and tell you which one to build first. Book a Discovery Call.
FAQ
Choose the first use case for a custom AI app by scoring candidates on value to the value creation plan, frequency, data readiness, output shape, a named owner, and the cost of an error. Build first where value is high and the data is ready. High-value ideas with poor data become data projects first.
The timeline for a custom AI app depends mostly on how well the use case is scoped and how many systems it connects to. A single workflow with one or two integrations moves much faster than a platform that spans many teams. Short build cycles with working software early make the real pace visible within the first few weeks.
Build a custom AI app when the workflow depends on the company's own data, rules, and systems, and when it moves a line of the value creation plan. Buy or configure an off-the-shelf tool when the task is generic, such as meeting notes or general research. The use case matrix in Step 1 helps sort the two.
A custom AI app needs a passing eval suite, SSO and role-based access, a load test, audit logging, user documentation and training, and a named owner with runbooks before it launches. Those pieces let the team prove the app works, defend its data access, and keep it working after the build team steps back.
The portfolio company should own a custom AI app after launch: the source code, prompts, eval set, and documentation should live in repositories it controls. A named person on the company's team should own its roadmap, with a development partner handling tuning and new releases if the team lacks engineering capacity.
