Put working AI inside the software your team already uses
AI software integration is the work of getting language-model capability into the systems you already run — a summarize button in your ticketing tool, extraction inside your intake form, classification in your ERP workflow — instead of asking your team to adopt yet another standalone app. It closes the gap between 'we pay for AI tools' and 'AI shows up in our daily work.'
Willowark treats the model as one component behind a well-defined interface, not the architecture. A thin service sits between your application and the model provider, handling prompt versioning, schema-validated structured outputs, caching, and fallbacks — so your product code calls a stable internal API and never has a prompt string buried in it.
AI & Intelligent AutomationHow the work gets done
The same way every time: scope, build, hand over.
The engineering details decide whether the feature holds up. Structured outputs are validated against a schema and retried on failure, so downstream code never parses free text. Responses stream where latency matters, repeated calls hit a cache, rate limits and spend caps are enforced at the service layer, and requests carrying sensitive data are scrubbed or routed according to your data-handling rules.
Every integration ships with an evaluation harness: recorded inputs with expected outputs, run in CI, so a model upgrade or prompt change gets tested like any other code change. In production we track latency percentiles, token spend per feature, and output quality flags — turning 'is the AI working?' from a vibe into a dashboard.
Scoping an integration starts with the feature, not the model. We identify where in your workflow the output will be used, who sees it, and what happens when it is wrong, because those answers set the accuracy bar and the latency budget. A summary shown to a support rep can tolerate an occasional miss; a classification that routes an order cannot. From there we weigh the trade-offs: a larger model is more accurate but slower and more expensive per call, streaming improves perceived speed but complicates error handling, and caching cuts cost but only for inputs that repeat. Each choice is written down so it can be revisited.
The failures we see most are quiet ones: a prompt edited in production with no record of what changed, a provider deprecating a model version, output that drifts as the input mix shifts, and spend that climbs because nobody set a cap. We design against all four. Prompts live in version control and deploy through the same pipeline as code, provider bindings are pinned and upgraded deliberately, eval results are tracked over time, and budgets are enforced in the service layer. At handover your developers get the service, the eval suite, and a guide to adding features, so the next one is a pull request, not a project.
Scope it in writing
What we agree before work starts
- AI service layer with a stable internal API your applications call
- Prompt and model version management with schema-validated outputs
Build with checkpoints
Working results, not slide decks
- Evaluation suite that runs in CI and gates model or prompt changes
- Caching, rate limiting, and per-feature cost tracking
Hand over something you own
Documentation, source, and training
- Data handling controls aligned to your privacy and security requirements
- Developer documentation and a walkthrough for adding new AI features through the service layer
Sound familiar?
Where ai software integration earns its keep.
A ticketing system where every inbound case arrives pre-classified and summarized
A CRM that drafts follow-up emails from call notes in the rep's own voice
An intake portal that extracts structured fields from whatever customers upload
An ERP workflow that flags anomalous orders with a plain-language explanation
Ask about AI Software Integration
Describe the problem. Get a straight answer.
One line is enough. An engineer replies within a business day.
Related work
AI operating software for training operations
Components:
- Schedulers (training ops): The people running the training operation.
- Operating software (AI assistance): The operating software, with AI assistance built in.
- Cloud services (distributed): Distributed cloud services behind the application.
- Training game (learning): The training game the software connects to.
Connections:
- Schedulers to Operating software
- Operating software to Cloud services
- Operating software to Training game
A global automotive manufacturer's training operation · Automotive
AI operating software for training operations
AI operating software for the manufacturer's training schedulers — removing manual scheduling labor and logistics tracking, saving hundreds of hours per year — plus a training video game built to help trainers perform better. Used across the manufacturer's training organization.
Read the case study →Common questions
Asked before every ai software integration project.
Which AI provider should we build on?
It matters less than the abstraction around it. We build against an internal interface so the underlying model — Anthropic, OpenAI, or an open-weight model you host — can change without touching product code. The starting choice comes down to your accuracy, latency, cost, and data-residency requirements.
What happens to our data when it goes to a model?
That is a design decision we make explicitly, not a default we inherit. Commercial API terms generally exclude training on your data; where requirements are stricter, we scrub identifiers before requests, host models in your cloud tenancy, or keep certain data out of prompts entirely. The controls live in the service layer, enforced in code.
Models keep changing — will this be obsolete in a year?
The models will change; the integration should not. Because prompts, evals, and provider bindings live in one service, upgrading to a better model is a configuration change validated by your eval suite, not a rebuild.
Can you integrate AI into our legacy or custom-built software?
Usually, yes. The service layer we build exposes a plain HTTP API, so anything that can make a web request can call it, including older systems, internal tools, and desktop applications. Where the legacy system cannot call out at all, we work through its database, file drops, or a small adapter. The harder question is where the output should land, and we settle that in scoping.
How do we keep AI costs from getting out of hand?
By measuring and capping at the service layer from the first deployment. Every feature is tagged, so spend is reported per feature rather than as one provider bill, and each has a budget that degrades gracefully when hit rather than failing loudly. Beyond that, the usual levers are caching repeated inputs, routing simple requests to smaller models, and trimming what goes into the prompt. Most cost surprises come from features nobody was watching, so the reporting matters more than any single optimization.
Where this sits
AI Software Integration, inside a ai & intelligent automation system.
The lit component is the part of the system this service delivers; the rest is what it has to work with.
Hover or focus a component to see what it is and what it talks to. Arrow keys move between them.
Inbound documents and messages are ingested, an agent reasons with company knowledge and acts through the systems of record, and a person reviews the cases that need judgment.
Components:
- Inbound (email, PDFs, forms): The unstructured work arriving every day.
- Ingestion (extract, classify): Turns documents into structured fields with confidence scores.
- Agent (reasons, uses tools): A model with tools: it looks things up, decides, and acts — within limits you set.
- Knowledge (your docs): Company procedures and history, retrieved on demand.
- Systems of record (ERP, CRM): Where the work actually lands.
- Reviewer (exceptions): The person who sees what the agent was unsure about.
Connections:
- Inbound to Ingestion over email
- Ingestion to Agent over events
- Agent to Knowledge over REST, both directions
- Agent to Systems of record over REST
- Agent to Reviewer over handoff
Strategy. Software. Systems.
Have a system that should exist?
Tell us what your operation is doing manually, what isn't connected, or what you're trying to build. We'll tell you plainly whether and how we can help.