AI opportunity review
Workshop your team's repetitive work; score 3–5 ideas on ROI, data readiness, and build effort — written plan, not slides.
- When to skip AI entirely
- Quick wins vs. multi-quarter bets
- Model & hosting recommendations
AI helps when language, documents, or messy inbound data slow your team down — not when a simple form rule would do the job. Evolva scopes chatbots, copilots, and workflow automation against real baselines: tickets per agent, minutes per intake form, leads stuck in inbox limbo. We integrate with HubSpot, Slack, email, and your admin panel on OpenAI, Anthropic Claude, or AWS Bedrock, with human review on anything that touches money, compliance, or customer commitments.
Most failed AI projects skip two steps: picking a workflow you can measure, and wiring outputs back into CRM, ticketing, or ERP fields people actually use. We have shipped 250+ software projects across 15+ industries — the same delivery discipline applies here. Start with an opportunity review that ranks use cases by data readiness and effort, or jump straight to one automation with a defined metric.
We are direct about limits. Generative models are weak at guaranteed arithmetic, long-horizon planning, and replacing regulated judgment calls. They are strong at drafting from templates, classifying intent, summarizing threads, and extracting structured fields from PDFs — especially with retrieval over your approved docs and guardrails on tool access.
Opportunity reviews, copilots, and workflow automation — measured against baselines, not demos.
Human-in-the-loop where accuracy matters; AWS and model choices you can operate after handoff.
Six delivery paths — often starting with review or one workflow, then expanding what proves value.
Workshop your team's repetitive work; score 3–5 ideas on ROI, data readiness, and build effort — written plan, not slides.
Answer FAQs, collect intake fields, and escalate with transcript + CRM link — bounded topics so the bot does not improvise policy.
Slack, Teams, or web UI search over docs you control — answers cite sources so employees verify before acting.
Pull structured data from attachments and threads; route low-confidence rows to a human queue.
Combine deterministic rules with LLM steps — summarize calls, suggest deal stages, draft follow-ups for rep approval.
Auth-gated React experiences where AI is the feature — search, drafting, or recommendations with usage limits per plan.
Validate on real samples, integrate with staging, roll out with humans in the loop — then measure or stop.
Map workflows, baseline metrics, and data sources. Pick one measurable outcome for the first release — or stop if AI is not the lever.
Working demo on anonymized or sample inputs. You judge quality before we connect production credentials.
Auth, logging, guardrails, and staging rollout. Agents or staff review outputs until error rates meet your bar.
Track time saved, cost per task, and override rates. Double down on what works; cut what does not.
Models and infra you can operate — often on the same AWS account as your web and custom software.
Begin with clarity, ship one workflow, or build a product — same engineers as our custom software practice.
Best when leadership wants a prioritized backlog before hiring or buying tools.
One production path — triage, extraction, routing, or internal Q&A — integrated end-to-end.
Full UI product with auth, billing hooks, and ongoing tuning — copilot or customer-facing AI feature.
Straight answers — including when not to buy AI yet.