Surgery, not rewrite
We identify the 2 or 3 spots where AI adds real value. We don't replace what works — we amplify it.
Service · AI integration
Add AI to what already works — without rewriting it.
You don't have to tear the system down to win with AI. We identify where it adds real value, integrate surgically and measure ROI from day one. What worked yesterday still works today — now with superpowers.
The context
The temptation is to rewrite everything. It's almost never the answer.
You have a system that works, that your team knows, that users adopted. The pressure to "add AI" doesn't justify tearing it down. Most of the highest-value improvements come from embedding AI in specific spots of the existing flow — without touching what already runs well.
Surgical integration, not transplant.
Measurable ROI from day one
Before integrating, we define the success metric. If we don't move the needle, we don't reach production.
Human in the loop
Where the cost of error is high, AI suggests and a human approves. Where it's low, it runs on its own. Decided case by case.
Your data stays inside the perimeter
Self-hosted models, providers with BAA or upfront masking. We design for your compliance reality.
We respect your stack
We integrate with .NET, Java, Node, Python, GeneXus or whatever runs. We don't make you adopt a new framework for this.
Short deliveries
First production integration scoped tight. Then we iterate with metrics in hand.
The capabilities that move the needle most.
Contextual assistance
A copilot embedded in key screens of your system, answering with your business data — not a generic encyclopedia.
Process automation
Flows that used to involve three people are handled by an agent — with escalation to a human when needed.
Data enrichment
Auto-classification, entity extraction, anomaly detection. Your data goes in the same; it comes out with metadata that's actually useful.
Semantic search
Search by intent instead of exact words. RAG over your tickets, documents, contracts, KBs or internal knowledge base.
Summarization and generation
Meeting minutes, ticket replies, technical docs, proposals. AI drafts; a person validates.
Triage and prioritization
Classify tickets, leads, alerts or incidents by real severity — not by arrival order.
From mapping to production integration.
- 01
Opportunity mapping
We work with your team to identify where AI adds — and where it doesn't. We come out with 2 or 3 candidates prioritized by impact and feasibility.
- 02
PoC with real data
We assemble a working prototype on the strongest candidate, using masked real data. Measured against the agreed metric.
- 03
Integration into the system
We connect to the production flow with a feature flag and gradual rollout. Full observability: latency, cost, quality, adoption.
- 04
Measurement and tuning
Supervised production with adjustments. We tune prompts, thresholds and models against real data, not assumptions.
- 05
Handoff and evolution
We leave a runbook, operations metrics and tuning playbook. Your team can run it; if you'd rather we keep going, we do.
Who this integration serves.
We'd rather say no than oversell. If something here doesn't add up for you, we'll talk it through on the first call.
It makes sense if…
- You have a working system or process and want to gain efficiency without rebuilding.
- You ran an AI PoC in dev and now need to take it to production for real.
- Your ticket, mail, document or alert volume grew and the team is overwhelmed.
- You have valuable data (tickets, contracts, KBs, logs) that nobody searches because searching is a slog.
- You operate in a regulated industry and need AI with strict controls — self-hosted models, audit, human in the loop.
It's not for you if…
- You want "AI" without a specific problem. Let's talk first without contract — we don't charge for exploration.
- You want a generic web chatbot for marketing. There are cheaper SaaS for that.
- You're building a new product from scratch: the custom-software service fits you better.
Why this service exists
Many teams ask us the same thing: “we have a system that works, we have data, and now we have to add AI. But we can’t rewrite everything.” Exactly. And they shouldn’t.
Most of the value AI can add to an existing system comes from targeted integrations in the flow: a copilot on the screen where the user works, a classifier that prioritizes the ticket queue, an extractor that pulls data from PDFs that used to be loaded by hand. Small things in technological impact, big in business impact.
How we think about scope
Before we put an AI model anywhere, we ask two uncomfortable questions:
- Which metric will move? If we can’t name it, we don’t start.
- What happens when it fails? If we can’t answer that well, the integration doesn’t reach production.
This sounds obvious and most AI projects skip one or both.
What’s left when we leave
A production integration with metrics, an operations runbook, a tuning playbook and quality code. Your team can maintain it, evolve it or shut it down — without depending on us. If you’d rather we keep going, that’s a separate contract that doesn’t condition anything we delivered.
What people ask before integrating.
Which tech stack can I count on?
We integrate against any modern backend via API — REST, gRPC, queues or DB triggers. We work over .NET, Java, Node, Python, PHP, GeneXus and some less common legacy systems. If your stack is more unusual, we'll discuss it during discovery.
Which AI models do you use?
Depends on the case. By default we evaluate Claude (Anthropic), OpenAI and self-hosted open source (Llama, Mistral, Qwen). We pick by precision, latency, cost and compliance. The architecture stays provider-agnostic — switching models doesn't force you to redo the integration.
How is inference cost controlled?
Four levers: pick the smallest model that solves the case, cache stable results, limit context and batch when volume allows. We leave you a dashboard with cost per operation and alerts before anything spikes.
Does my sensitive data leave the perimeter?
Depends on what we agree. Options: self-hosted models (nothing leaves), providers with BAA signed, upfront PII masking. For regulated industries, we design the option that passes your security review before we write code.
What if the AI gets it wrong?
We treat it as expected, not as an accident. Critical output goes through a human before impact; non-critical has confidence thresholds, fallbacks and logs to review. The integration includes a plan for when it fails.
How long does a typical integration take?
We cover mapping, PoC, integration and measurement for the first capability in production. Calendar is closed at kickoff per scope. Subsequent integrations are faster because the platform is already in place.
Can you integrate AI on top of GeneXus applications?
Yes, it's part of our DNA. We do it with webhooks, procedures consuming APIs, KBDeepdive integration for semantic search over the KB, or lateral layers exposing AI without touching core objects. Depends on the case and your GeneXus version.
Other services
Have a process asking for AI?
An initial call is enough to identify if there's a case, size it and give you an honest opinion — including "don't do it", if that's the answer.