Most teams running AI initiatives recognize at least one of these situations.
AI tool selection drives the project instead of business needs
The conversation centers on platform selection, and the operational bottleneck that needed to be solving ends up defined around the tool’s capabilities.
Why AI process transformation works
“Most teams know they have manual work that should not be manual. What they need is someone to map it, score it, tell them where to start, and then build it. That is what this process covers, from the first workshop to a deployed solution running in production.
And the results we see across engagements are consistent: processing time drops, manual steps disappear, and the team gets capacity back.”
Yevhen Kliukin
Agentic AI Transformation Lead
What changes with a structured approach
Check out the figures that reflect typical results from Geniusee’s automation engagements:
| Situation | Current state | After transformation |
| Lead qualification | The sales team manually reviews every inbound lead, 2-3 hours per day | AI scores and routes leads in real time, the team focuses on the high-priority pipeline, saving 2+ hours per day |
| Document processing | Staff extract data from contracts and forms manually, 4 hours per day | Automated extraction and validation, human review takes 30-40 minutes per day |
| Weekly reporting | Analyst pulls data from 5 systems and compiles the report, 3 hours per week | Report generates automatically, analyst reviews and sends in 20 minutes per week |
| Customer feedback | Support team reads and categorizes feedback by hand, several hours per day | AI routes issues by type and urgency, triage time down by 70% |
| CV screening | HR reviews 80+ applications per role, 6-8 hours per opening | AI screens against criteria and delivers a shortlist with summaries, review takes under 1 hour per role |
| Operational coordination | Team tracks task status across Slack, email, and spreadsheets, 1-2 hours per day | Agentic workflow monitors status and sends updates automatically |
Computer-based work involves processing and transforming information: reading a document, extracting what matters, making a decision, passing it forward. AI agents take over that cycle across a wide range of business processes. Below are the types we build most often.
Preparation
Before the workshop, we talk to the people who actually do the work. What the team handles day to day, where things accumulate, what keeps getting moved to tomorrow. That conversation sets the scope for everything that follows.
Discovery and prioritization
We examine each recurring activity: how long it takes, how often it runs, and how much of it is routine versus judgment. In our experience, most departments have 10 to 15 candidates. The session scores each one and ends with a shortlist of 1 to 3 with the strongest business case.
Solution design
We conduct a detailed analysis of the selected workflow: mapping who is involved, which systems it touches, what inputs it receives, what outputs it produces, and where human judgment currently determines the outcome. That analysis defines what to automate, what to keep in-house, and how to connect the components.
Implementation
We build the solution, connect it to your systems, and deploy it live, without sandbox handoff. The team starts using it on real tasks from the first day.
Evaluation
We check the agreed metrics against actual results: time per task, volume, and error rate. For outputs that are harder to quantify, a structured review process supplements the data.
Follow-up
Once the first automation is live, the next candidate is already scored and waiting. The same approach applies to the next department or to refining what is already running in production.
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Forethought partnered with Geniusee to extend and integrate its AI-powered support platforms (Assist and Discover) into complex enterprise environments. We helped enhance real-time agent assistance, streamline multi-system connectivity, and deliver a unified support experience across channels. The result is a scalable AI agent ecosystem that accelerates resolution times, boosts team productivity, and powers more consistent customer experiences.
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Case detailsPhase 1. Pilot discovery (free of charge)
A focused workshop with your team, conducted per department or stakeholder group. We identify recurring high-effort activities, score automation candidates, and recommend the strongest first implementation target. You receive a structured report within 1 week.
Phase 2. Implementation
Detailed workflow analysis of the selected use case, solution design, development, and integration into your existing operations.
Proven AI delivery across industries
Geniusee has delivered AI-powered products across recruiting, content generation, voice synthesis, law enforcement reporting, and audit automation. For a UK-based HR client, the AI recruiting platform we built cut manual screening time by 1.5 to 2 hours per day. For Compose AI, we built a Chrome extension integrated with OpenAI that generates content across Gmail, Superhuman, and other tools.
Agentic AI and multi-modal workflow automation architecture
The architecture goes beyond a single model call. Each agent decomposes tasks into steps and selects the right tools: APIs, document stores, and databases. The model checks its own output before proceeding and hands off to a human reviewer when the workflow requires it. Then, it applies RAG pipelines, vector search, and model orchestration based on data and process requirements.
Flexible engagement models for every automation roadmap
The engagement format follows the work. A focused discovery and prioritization session, a dedicated team for full automation implementation, or long-term support for AI-assisted workflows and agentic systems — each is structured around your roadmap, budget, and operational priorities.
Certified AWS Partner delivering secure, scalable cloud-native solutions.
ISO-compliant processes ensuring quality, security, and reliability.
Trusted integration partner for financial data connectivity and open banking.
Team of ISTQB-certified QA engineers for world-class software testing.
Consistently rated ★5.0 by clients for reliability and delivery excellence.
Accredited partnership supporting advanced testing and continuous QA automation.
How long does the discovery phase take?
The workshop runs 1 to 2 working days per department or stakeholder group, including preparation. The output report is delivered within 1 week. If multiple departments are involved, each runs as a separate focused session to keep the analysis and prioritization specific to that team’s workflows.
Do we need to prepare anything before the workshop?
We ask you to identify a department lead or relevant stakeholder group and share a rough list of recurring team activities beforehand. We design the session structure, run the workshop, and produce the written output.
What types of companies does this work for?
Any organization where teams handle recurring, structured, or high-volume work. We have applied this approach across fintech, edtech, retail, real estate, and professional services. The discovery process is scoped to one department at a time, so company size affects the number of sessions rather than the method itself.
Is the discovery really free?
Yes. If no strong automation candidate is found, nothing is owed.
What AI technologies do you use?
It depends on the workflow. Most automation projects involve agentic AI systems: models that plan tasks, call external tools, process documents or structured data, and verify their own outputs before passing results forward. We work across OpenAI, Anthropic Claude, Google Gemini, and Amazon Bedrock, paired with orchestration layers, vector databases, and custom integrations into your existing systems. For workflows where output consistency is critical, we bring in our prompt engineering services as part of the build.
How do you measure success?
Before the build, we agree on what good looks like: time per task, volume per day, error rate. After deployment, we check those numbers. Complex outputs get a structured review on top of the metrics.
What happens after the pilot?
You get a ranked shortlist and a clear first target. Some clients build with us, some take it in-house. Either way, you leave with enough to make the decision and move forward.
How is this different from robotic process automation?
Robotic process automation handles rule-based tasks on fixed inputs with predictable structure. AI automation handles workflows where the input varies, judgment is involved, or the process requires understanding context. Many automation roadmaps include both, with RPA handling structured repetitive steps and AI models handling the parts that require interpretation or decision-making.
