Aevita Solutions

Every solution is a working system, not a slide deck.

Ten ways we apply AI engineering and intelligent automation to real operations — each delivered as production software with human control, security boundaries, and a measurable outcome.

01 / 10

AI Agents & Autonomous Systems

Software teammates that execute multi-step work across your tools — with humans approving what matters.

The business problem

High-volume operational work — triage, research, data entry, follow-ups — consumes skilled staff time, and simple scripts break the moment a task requires judgment or spans more than one system.

What Aevita builds

Aevita designs and deploys goal-directed agents with defined tool access, structured memory, and explicit escalation paths. Each agent operates inside a permission boundary you set, executes multi-step tasks across your systems, and hands off to a person whenever confidence drops or a decision exceeds its mandate.

Systems involved

Slack
Gmail
Salesforce
Postgres
Notion
Jira

How it works

Step 1
Trigger
An event, schedule, or request starts the agent.
Step 2
Plan
The agent decomposes the goal into concrete tool calls.
Step 3
Act
It executes across connected systems, logging every step.
Step 4
Verify
Results are checked against acceptance rules before completion.

Human control & approvals

Approval gates on any write action you designate; confidence thresholds that route edge cases to a named owner; a full action log for review and rollback.

Security considerations

Scoped, least-privilege credentials per agent; hard boundaries on which data stores an agent can read or write; immutable audit logs of every tool invocation.

Expected operational outcome

Routine multi-system work runs continuously without headcount growth, while your team reviews exceptions instead of executing every step by hand.

Book an AI Strategy Call

02 / 10

Enterprise Workflow Automation

End-to-end business processes rebuilt as reliable, observable pipelines instead of email chains.

The business problem

Core processes — order-to-cash, employee onboarding, procurement approvals — live in inboxes and spreadsheets. Handoffs stall, status is invisible, and errors surface only after a customer or auditor notices.

What Aevita builds

Aevita maps the real process, then rebuilds it as a durable workflow with typed inputs, retry logic, and state you can inspect at any moment. Steps that need judgment become explicit approval tasks; everything else runs automatically with alerts on failure.

Systems involved

SAP
NetSuite
Slack
Gmail
DocuSign
Postgres

How it works

Step 1
Map
Document the current process, owners, and failure points.
Step 2
Model
Encode it as a durable workflow with clear states.
Step 3
Connect
Wire in the source systems via authenticated integrations.
Step 4
Operate
Run with monitoring, retries, and exception queues.

Human control & approvals

Named approvers on financial or contractual steps; pause-and-resume on any stage; dashboards showing exactly where each in-flight item sits.

Security considerations

Service accounts with least-privilege scopes per system; data kept inside your environment boundaries; step-level audit trails for compliance review.

Expected operational outcome

Cycle times drop from days to hours, handoffs stop leaking, and process status becomes a query instead of a meeting.

See Business Workflows

03 / 10

Generative AI Applications

Purpose-built generative products — drafting, summarization, structured extraction — grounded in your data.

The business problem

Teams experiment with general-purpose chat tools, but outputs are ungoverned, disconnected from company data, and impossible to standardize — so quality varies with whoever wrote the prompt.

What Aevita builds

Aevita builds production generative applications with engineered prompts, evaluation suites, and guardrails: drafting assistants, report generators, structured-data extractors, and domain copilots that pull from approved sources and produce output in your formats.

Systems involved

Slack
Google Drive
Notion
Postgres
Snowflake

How it works

Step 1
Scope
Define the task, output format, and quality bar.
Step 2
Ground
Connect approved data sources and style guides.
Step 3
Evaluate
Test outputs against a scored evaluation set.
Step 4
Ship
Deploy with usage analytics and feedback loops.

Human control & approvals

Human review checkpoints before generated content reaches customers; versioned prompts so changes are deliberate; feedback capture that feeds continuous evaluation.

Security considerations

Model access routed through a gateway you control; sensitive fields masked before inference; prompt and completion logs retained for audit.

Expected operational outcome

Consistent, on-brand generative output at scale — measured against an evaluation suite rather than judged anecdotally.

Book an AI Strategy Call

04 / 10

RAG & Enterprise Knowledge Systems

Retrieval-augmented systems that answer from your documents with citations — not from a model's memory.

The business problem

Institutional knowledge is scattered across wikis, drives, tickets, and PDFs. Employees re-ask answered questions, and generic chatbots hallucinate because they cannot see your actual documentation.

What Aevita builds

Aevita builds retrieval pipelines — ingestion, chunking, embedding, and permission-aware search — so answers are generated from your verified content and every response carries citations back to source documents.

Systems involved

Confluence
SharePoint
Google Drive
Postgres
Snowflake
Slack

How it works

Step 1
Ingest
Index documents with metadata and access rules intact.
Step 2
Retrieve
Match each question to the most relevant passages.
Step 3
Generate
Compose an answer grounded only in retrieved content.
Step 4
Cite
Return sources so every claim can be verified.

Human control & approvals

Curators approve which repositories enter the index; low-confidence answers are flagged rather than guessed; content owners see what questions their documents answer.

Security considerations

Retrieval respects the source system's existing permissions per user; embeddings and indexes stay inside your data boundary; query logs support access audits.

Expected operational outcome

Employees and customers get cited, current answers in seconds, and subject-matter experts stop being the bottleneck for routine questions.

Book an AI Strategy Call

05 / 10

Custom AI Platforms

Your own AI product surface — multi-model, multi-tenant, and owned by you rather than rented.

The business problem

Off-the-shelf AI tools stop at the edges of your business model. When AI is core to your product or operations, you need infrastructure you control — model routing, tenancy, billing, and evaluation — not a stack of subscriptions.

What Aevita builds

Aevita architects and builds full AI platforms: model gateways with provider failover, tenant isolation, usage metering, evaluation harnesses, and admin consoles — engineered on modern web infrastructure and handed over with documentation and training.

Systems involved

Postgres
Snowflake
Redis
Vercel
AWS
Stripe

How it works

Step 1
Architect
Design the platform around your tenancy and cost model.
Step 2
Build
Implement gateway, data layer, and product surfaces.
Step 3
Harden
Load-test, add observability, and rehearse failure modes.
Step 4
Hand over
Transfer ownership with runbooks and team training.

Human control & approvals

Admin consoles for feature flags, model selection, and spend limits; staged rollouts per tenant; kill switches on any AI capability.

Security considerations

Tenant-level data isolation; secrets managed in your vault, never in code; per-request audit logs covering model, input class, and caller identity.

Expected operational outcome

A platform asset you own and extend — with unit economics, reliability, and roadmap under your control instead of a vendor's.

Book an AI Strategy Call

06 / 10

CRM & ERP Integrations

Bidirectional, validated data flow between your systems of record — no more swivel-chair operations.

The business problem

Sales, finance, and operations each trust a different system. Records drift apart, teams re-key data between screens, and leadership reconciles conflicting numbers instead of acting on one.

What Aevita builds

Aevita builds hardened integration layers between CRMs, ERPs, and data warehouses: field-level mappings, validation rules, conflict resolution policies, and AI-assisted matching for the records that never line up cleanly on their own.

Systems involved

Salesforce
HubSpot
SAP
NetSuite
Postgres
Snowflake

How it works

Step 1
Audit
Inventory objects, fields, and ownership across systems.
Step 2
Map
Define canonical records and transformation rules.
Step 3
Sync
Run event-driven, bidirectional synchronization.
Step 4
Reconcile
Surface mismatches for review instead of silent drift.

Human control & approvals

Dry-run modes before any bulk write; human review queues for ambiguous record matches; per-field sync rules your admins can adjust.

Security considerations

Integration users scoped to only the objects they touch; encrypted transport between systems; change-data logs showing who or what modified every record.

Expected operational outcome

One trusted version of each customer, order, and invoice — with teams working from live data instead of exports.

Book an AI Strategy Call

07 / 10

AI-Powered Marketing Automation

Lifecycle marketing that segments, personalizes, and follows up automatically — under brand-safe controls.

The business problem

Marketing teams have more channels than hands. Leads go cold waiting for follow-up, segmentation is guesswork, and campaign production is a bottleneck that flattens personalization into batch-and-blast.

What Aevita builds

Aevita connects your CRM, product data, and messaging channels into an automation layer: AI-scored segments, personalized content generation inside brand guardrails, and lifecycle journeys that adapt to behavior instead of running on fixed timers.

Systems involved

HubSpot
Salesforce
Gmail
Snowflake
Segment
Slack

How it works

Step 1
Unify
Consolidate lead and behavior data into one profile.
Step 2
Segment
Score and group audiences on live signals.
Step 3
Personalize
Generate on-brand variants per segment.
Step 4
Optimize
Measure, test, and reallocate automatically.

Human control & approvals

Marketers approve copy templates and tone rules before anything sends; frequency caps and suppression lists enforced in the pipeline; every send traceable to the rule that triggered it.

Security considerations

Consent and opt-out state honored at the data layer; customer PII confined to approved systems; access to audience data logged and role-restricted.

Expected operational outcome

Faster follow-up, sharper segmentation, and campaign volume that scales with data — not with headcount.

Book an AI Strategy Call

08 / 10

Document Intelligence

Contracts, invoices, and forms converted into structured, validated data — automatically.

The business problem

Critical business data arrives trapped in PDFs, scans, and email attachments. People re-type it into systems, errors creep in, and the documents themselves become an unsearchable archive of risk.

What Aevita builds

Aevita builds extraction pipelines that classify incoming documents, pull structured fields with AI, validate them against business rules, and post clean records into your systems — routing anything ambiguous to a human review queue.

Systems involved

Gmail
SharePoint
DocuSign
SAP
NetSuite
Postgres

How it works

Step 1
Classify
Identify document type and route accordingly.
Step 2
Extract
Pull fields, tables, and clauses into structured form.
Step 3
Validate
Check values against rules and reference data.
Step 4
Post
Write verified records to downstream systems.

Human control & approvals

Confidence thresholds decide what posts automatically versus what a person confirms; reviewers correct in-line and the pipeline learns from corrections; nothing enters a system of record unvalidated.

Security considerations

Documents processed inside your storage boundary; field-level redaction for sensitive values; a complete chain of custody from source file to posted record.

Expected operational outcome

Document-heavy processes shrink from days of manual entry to minutes of review, with extraction accuracy you can measure and audit.

Book an AI Strategy Call

09 / 10

AI Customer Experience

Support and service that resolves instantly where it can — and escalates gracefully where it should.

The business problem

Customers expect immediate, accurate answers on every channel, but support teams are buried in repetitive tickets while complex cases wait in the same queue as password resets.

What Aevita builds

Aevita builds AI service layers grounded in your help content and account data: assistants that resolve routine requests end-to-end, draft responses for agents on harder ones, and summarize context so escalations start informed instead of from zero.

Systems involved

Zendesk
Intercom
Salesforce
Slack
Postgres

How it works

Step 1
Understand
Classify intent and load relevant account context.
Step 2
Resolve
Answer from verified content or execute the fix.
Step 3
Escalate
Hand complex cases to agents with a full summary.
Step 4
Learn
Feed resolutions back into the knowledge base.

Human control & approvals

You define which intents the AI may resolve autonomously; agents review AI drafts before sending where required; sentiment triggers force human takeover.

Security considerations

Customer data accessed read-only unless a specific action is authorized; conversations logged for quality audit; account operations gated behind verified identity.

Expected operational outcome

Faster first response and resolution on routine volume, with human agents concentrated on the conversations that genuinely need them.

Book an AI Strategy Call

10 / 10

Digital Transformation

A sequenced modernization program — from process audit to running systems — not a strategy PDF.

The business problem

Leadership knows the operation runs on legacy systems and manual glue, but transformation efforts stall: too broad to start, too risky to sequence, and too abstract to show value early.

What Aevita builds

Aevita runs transformation as engineering: an operational audit that maps systems and manual effort, a prioritized automation roadmap scored by impact and risk, and delivery in production increments — each phase shipping working systems before the next begins.

Systems involved

SAP
Salesforce
Snowflake
Slack
Postgres
AWS

How it works

Step 1
Audit
Map processes, systems, and manual effort honestly.
Step 2
Prioritize
Rank initiatives by impact, risk, and dependency.
Step 3
Deliver
Ship automation and AI capability in phases.
Step 4
Embed
Train teams and transfer operational ownership.

Human control & approvals

Steering checkpoints between phases with go/no-go decisions; legacy processes kept running in parallel until replacements prove out; success metrics agreed before each build starts.

Security considerations

Migration plans that define data boundaries before any data moves; least-privilege access for every new component; audit logging designed in from the first phase, not retrofitted.

Expected operational outcome

Measurable operational gains at each phase and a modern, automated core — delivered as running software rather than recommendations.

See Business Workflows

Not sure where to start?

Most engagements begin with a short working session: we map your highest-friction process, identify the systems involved, and propose the first automation worth building.

Book Your Free AI Assessment Get Your Automation Roadmap