AI in real business processes. Hours saved, costs reduced.
We implement AI in real business processes — document handling, reporting, data analysis, customer communication, internal knowledge access. Working AI workflows that measurably save hours and reduce cost. Not "AI integration" claims on a pitch deck. We work in production environments including regulated industries (pharma marketing under MLR review) and IP-core products where the AI layer is the core engineering function.
What this service covers.
AI Integration at Baryvo is engineering work first and modeling work second. Most of our delivery is the surrounding system — the retrieval layer, the routing and safety logic, the audit trail, the deployment surface, the evaluation harness — because that is what determines whether an AI feature stays in production after the demo. We measure success in hours saved and costs reduced, not in model benchmarks.
The work spans general business automation (document workflows, reporting, customer comms) through to compliant production agents in regulated industries. For pharma marketing teams operating under MLR, we have shipped architectures with dual-hemisphere routing, response-level audit, and content traceability built in from day one — because regulated AI is not retrofittable, it has to be designed.
- 01Document handling automation — intake, classification, extraction, routing
- 02Reporting automation — AI-driven summaries, anomaly detection, executive briefings
- 03Data analysis copilots over proprietary data sources
- 04Customer communication — first-response, classification, escalation routing
- 05Internal knowledge bases — RAG over Notion, Confluence, Drive, Slack with role-aware access
- 06Compliant LLM agents for regulated content with response-level audit trails
What clients gain.
The benefit of contracting AI integration as a system-level service rather than a model-tuning engagement is shipping. We come in with a known stack, a known evaluation pattern, and a track record of architectures that have already passed medical and legal review in regulated industries. The first thirty days are not spent learning whether RAG is the right approach.
A recurring situation: a marketing or product team has built an impressive AI prototype with a single contractor, but cannot get it past Compliance, Security or Legal. We sit between the prototype and the production environment and rebuild the parts that need to be auditable — without throwing away the parts that already work.
- AProduction architectures, not notebook prototypes
- BAudit trail at the response level — every output reconstructable from source IDs
- CCompliance-aware design from day one, not retrofitted
- DVendor-flexible across OpenAI, Anthropic, open-weight models depending on the use case
- EEngineering ownership — the team that builds it operates it for the first quarter
- FKnowledge transfer to your team built into the timeline, not sold as an addon
How we work.
AI engagements proceed in four phases. The first two are fixed discovery and design; the third and fourth are continuous implementation and operation.
Discovery
A senior consultant maps the use case, the constraints (compliance, security, performance, cost), the existing data sources and the realistic success criteria. Output: a written architecture proposal with a buy-vs-build call.
Architecture & spec
Component diagram, evaluation plan, audit-trail design, cost model. Signed off before any production code is written. This phase exists because AI work fails most often from missing constraints, not missing engineering.
Build & roll out
Implementation against the spec, with weekly demos against the evaluation harness. Production rollout is staged — internal test users, then department, then full deployment.
Operate & iterate
We run the system for at least one quarter after go-live, including the evaluation cycle and model-drift monitoring. After that, knowledge transfer to your team or continuing operations under retainer.
Who this is for.
Best fit
This service is best suited to:
- Operating companies whose teams spend significant hours on document handling, reporting or customer ops
- Pharma marketing teams building HCP-facing AI under MLR constraints
- Companies with proprietary knowledge that should be searchable but isn't
- Executive teams whose reporting cadence is bottlenecked by manual synthesis
- Behavioural analytics platforms where the AI layer is the core IP, not a feature
Discuss an AI integration engagement.
Most AI conversations with us start with a written brief — a paragraph on what you have today, what you want users to be able to do, and what compliance environment you operate in. A senior consultant responds within one business day with a structured next step.