Can AI be added to our current product?

Usually, and it beats a separate tool. We assess your data and architecture, then build the feature inside the product your users already use. Adoption is far higher when nobody has to learn a second system.

Can company data be kept private?

Yes. Provider configurations that exclude your content from training, retrieval indexes in infrastructure you control, and regional processing where required. If a use case cannot meet your obligations, we say so before building it.

How do you reduce incorrect outputs?

Grounding answers in your documents with visible sources, constraining outputs to validated structures, and building the system to decline when unsupported. No system reaches guaranteed accuracy, which is why review paths exist.

Which model should we use?

Often more than one. Cheap fast models handle extraction and classification; harder reasoning justifies a stronger one. We route per task and keep a fallback, so an outage or price change isn't your problem.

How is AI cost controlled?

Spend caps, per-customer rate limits, caching for repeated queries, and cost logged per feature so you can see what each capability actually costs.

Does every output need human review?

No, and requiring it everywhere removes most of the benefit. We set the threshold by consequence: internal search runs unattended, anything touching a customer's money or entitlements goes through approval.

AI Development and Integration

AI integration built for production

We add AI where it measurably improves the work, then build what a demo skips: retrieval from your data, validation, human approval and cost ceilings.

Assess an AI use case View our work

Retrieval, model routing and human-review flows running against real business data.

What we build

AI features inside real products, with oversight, evaluation, cost control and fallbacks.

Knowledge assistant

Retrieval-augmented assistants

pgvector alongside your relational data, not a second database

AI features in existing products

Behind a flag, measured before release

Predictive analytics

Task-scoped agents

Explicit tool definitions, validated arguments

Document and voice processing

Confidence thresholds route to human review

Automated workflow builder connecting capture, validation, CRM update and notification steps

Workflow automation

Evaluation and guardrails

Prompt versioning, regression runs on provider changes

Cost control and fallbacks

Cost logged per feature

Thinking about a build like this for your business?

Assess an AI use case

Tech stack

The tools we build it on.

Proven, well-supported technology chosen to fit the work, not chase a trend. You own all of it.

Python
TypeScript
OOpenAI
CClaude
LangChain
FastAPI
PostgreSQL
Ppgvector
PyTorch

Why teams choose us

The parts that matter once the work is real.

People stay accountable

We design where a human approves, not whether. Anything affecting money, entitlements or health is proposed by the system and decided by a person.

Grounded, or it says so

Responses cite their source so a reader can verify. Where no grounded answer exists, the system is built to decline rather than produce a plausible one.

Your data stays yours

Provider configurations that exclude your content from training, indexes in infrastructure you control, and a written account of what leaves your environment.

Cost is bounded by design

Spend caps and per-tenant limits from day one. An AI feature without a ceiling is an uncapped liability.

See how we’d approach your project

How we work

A clear path from first call to launch.

The same rigorous process every time, adapted where the work needs it. You see progress at every step.

01
Phase 01
Use-case assessment
Whether AI is the right tool, what good looks like, and what a wrong answer costs.
02
Phase 02
Data readiness
Retrieval quality follows source quality. We assess what to clean before building.
03
Phase 03
Evaluation set
Real questions with correct answers, agreed up front, so changes can be proven.
04
Phase 04
Build and measure
Behind a flag, scored against the evaluation set rather than judged on a few impressive examples.
05
Phase 05
Safeguards and launch
Spend caps, rate limits, fallback models and review paths in place before real users arrive.

Start a conversation about your project

Before you enquire

The questions worth asking before building with AI.

If yours isn't here, ask us directly. A real person reads every message and replies within one business day.

Emailinfo@xpansionit.com Phone+61 420 883 221

Usually, and it beats a separate tool. We assess your data and architecture, then build the feature inside the product your users already use. Adoption is far higher when nobody has to learn a second system.
Yes. Provider configurations that exclude your content from training, retrieval indexes in infrastructure you control, and regional processing where required. If a use case cannot meet your obligations, we say so before building it.
Grounding answers in your documents with visible sources, constraining outputs to validated structures, and building the system to decline when unsupported. No system reaches guaranteed accuracy, which is why review paths exist.
Often more than one. Cheap fast models handle extraction and classification; harder reasoning justifies a stronger one. We route per task and keep a fallback, so an outage or price change isn't your problem.
Spend caps, per-customer rate limits, caching for repeated queries, and cost logged per feature so you can see what each capability actually costs.
No, and requiring it everywhere removes most of the benefit. We set the threshold by consequence: internal search runs unattended, anything touching a customer's money or entitlements goes through approval.