AI Integration Services for Java Applications
We add AI features to Java systems you already run: search and answers over your own documents, drafts for a person to review, and agents that call your services within set limits. Each starts as a four-week pilot with an evaluation set and access controls, and reaches production only if the results hold.
Machine learning we have built and run
- 20-30 ms
- per recommendation in RecostreamThe product we built and operatedRecostream case study
- Acquired
- by GetResponse in December 2022Recostream recommendation engineRecostream case study
- Data quality
- anomaly detection for healthcare researchFlags the records that need investigationHealthcare research case study
- Best fit
- Java teams with a defined use case and accessible data
- Pilot
- Four-week target after scope and access are agreed
- Commercial model
- Time and materials pilot with a ballpark estimate; optional ongoing retainer
- You keep
- Code, evaluation results and operating documentation
AI use cases we integrate into Java systems
Search and answers over your data
Retrieval of relevant documents and records, with source references, designed around the permissions of the person asking.
Drafting and decision support
Summaries, responses or case notes prepared for a person to review. We agree what a useful answer looks like and which decisions stay with your team.
Bounded agent workflows
Model calls connected to approved tools and APIs, with action limits, human approval points and a way to stop the workflow.
Recommendations and classification
Machine learning for ranking, recommendations or triage, used where it beats a simpler rule on representative examples.
What the four-week pilot delivers
One agreed use case
A written scope, success measures, data-access requirements and a decision on whether AI is appropriate.
Integration in your environment
Java and Spring integration with agreed model providers, existing APIs, identity controls and deployment tooling.
Evaluation and safeguards
A representative evaluation set, quality and latency results, usage-cost measurements, logging and human approval where required.
Rollout and handover
Code in your repository, an operating guide and a release plan. Production rollout follows agreed acceptance criteria and your approval.
They have impressively good knowledge of AI issues. Very responsive to any amendments and findings. Very good communication. We received a finished project which could be implemented into production shortly after testing.
How the AI pilot runs
Agree the use case
Access, domain-expert time, evaluation criteria and the ballpark are confirmed before kickoff, along with any preparation the data needs.
Build the integration
We build the data path, the model calls and the controls, and show working software every week so your team can review its behavior early.
Evaluate and decide
Representative and failure cases are tested. Quality, latency and cost are reviewed together, and the evidence decides whether to improve, release or stop.
Operate or extend
Your team takes over with the code and the runbook, or an optional retainer covers further use cases and agreed operating duties.
How the pilot is priced
The ballpark estimate covers one use case and its agreed deliverables. The pilot is billed time and materials. Model usage and hosting costs are identified separately. Four weeks is the delivery target for a ready, bounded scope; data preparation, security approvals or additional integrations may change the plan.
Ongoing work uses a monthly retainer sized to the agreed workload. The retainer is optional.
Questions about AI integration
Which models and frameworks do you use?
Hosted or self-hosted models, chosen around your data policies, operating requirements and evaluation results. Spring AI is our usual way to call models from Java, and the design follows your existing stack.
How do you handle sensitive data?
We agree permitted data, providers, retention and logging before implementation. Retrieval must enforce user access rights, and logs must avoid exposing information beyond those rights. Your security and compliance teams review the proposed controls.
Does an older Java application need modernization first?
We assess the integration boundary and supported dependencies before recommending changes. A runtime upgrade, a separate service or an existing API may be appropriate; the pilot proposal explains the tradeoffs.
What if the pilot does not meet its acceptance criteria?
We report the results and remaining limitations. You can choose a revised scope, a simpler rules-based approach or no further development. Production rollout is not an automatic pilot outcome.
Can our own team operate the feature?
Yes. The handover includes code, configuration, evaluation cases and operating documentation. We agree who owns monitoring, model changes and incident response before release.
Do you build RAG and LLM integrations?
Yes. Most pilots are one of the two: retrieval-augmented generation (RAG) over your own documents and data, or LLM integration into an existing workflow, with the model called from your Java services through Spring AI. Our RAG development services start evaluation with retrieval, because retrieval quality usually decides the result more than the choice of model. Our LLM integration services end in a feature your team can operate, with prompts, retrieved sources and outputs logged.
Are you an AI consulting firm or an AI development company?
We are a Java engineering company that integrates AI into existing systems. Our AI consulting services end in working software: a pilot running in your environment, with its evaluation results. We do not train foundation models or build standalone chat products. In AI agent development services we start from the system the agent has to act on, and build bounded agents that call your existing services under the same access controls as any other client.
Do you offer generative AI integration services for regulated systems?
Yes. The pilot is designed for banking, fintech and insurance systems. Before implementation we agree permitted data, model providers, retention and logging with your security team, and a person reviews any output that feeds a regulated decision.
Related services and guides
Bring one AI use case to the call
Describe the workflow, the data it needs and the result you want to measure. We will propose a pilot scope and the preparation it needs.