What We Deliver
Working AI systems in production — not decks or proofs of concept that stall.
Agentic AI Solutions
AI Agents That Do Real Work
Genesis designs, builds and runs AI agents, AI assistants and agentic automation workflows that do real work inside a business — tied to business goals, connected to business systems, and safe to put in front of customers and staff.
Summary
Working Systems in Production
How We Build
On leading AI platforms (Anthropic, OpenAI, xAI, Google), as in-house agents on open-weight models, or a hybrid of the two.
What Makes It Different
We build and run agentic systems in our own products, so every recommendation comes from production experience.
The Goal
Measurable business outcomes — hours saved, revenue gained, errors removed and response times cut.
Why Agentic AI
Chatbots Answer. Agents Get Work Done.
Agents plan, call tools, read and write to business systems, check their own results and hand off to people when needed.
Most businesses still run critical processes on email threads, spreadsheets and manual copy-paste between systems. These are exactly the processes agents can take over.
The hard part is not the model. It is:
- Connecting the model to the right data and tools
- Making it reliable and safe
- Fitting it to how people actually work
That is where Genesis focuses.
Solution Areas
What We Build & Serve
Eight ways agentic AI creates leverage — from in-app assistants to multi-agent systems and the governance that keeps them safe.
01
Custom AI Assistants & Copilots
AI assistants built into a client's product or internal tools, which understand the business's own data and answer in plain language.
- In-app assistants for SaaS products, so end users can ask questions of their own data
- Internal copilots for sales, operations, HR, finance and support teams
- Assistants that take actions (create records, schedule, send, update), not just answer
- Role-based access: the assistant only sees and does what the user is allowed to
- Memory the user controls: proposed facts are kept only after user approval
- Structured, visual answers — tables, cards and charts, instead of walls of text
02
Agentic Workflow Automation
End-to-end business processes run by AI agents, with people approving the steps that matter.
- Document intake: read invoices, contracts, resumes, forms or emails, extract structured data and route it
- Monitoring: watch websites, inboxes, feeds or databases for changes and update records automatically
- Reconciliation: compare two systems, flag drift and propose fixes
- Reporting: scheduled agents that gather, analyse and deliver a summary to the right people
- Outreach and follow-up: personalised sequences with human review before sending
- Approval gates at every irreversible step (send, pay, delete, publish)
03
Goal-Driven Autonomous Agents
In-house agents with a fixed business goal, clear boundaries and measurable success criteria.
- A goal — e.g. "keep the release board in sync with the code repository"
- Tools — the specific actions it is allowed to take
- Guardrails — what it must never do, and when it must stop and ask
- Metrics — how success is measured and reported
- Runs on a schedule or on events (a new email, a new record, a webhook)
- Starts in shadow mode, then moves to write mode once its output is trusted
04
Custom MCP Servers & Connectors
Model Context Protocol (MCP) is the open standard that lets AI assistants such as Claude and ChatGPT use external tools and data. We build MCP servers that make a client's systems usable by AI.
- Product MCPs: let a SaaS product's customers use it from Claude, ChatGPT or other AI assistants
- Internal MCPs: expose company systems (databases, ERPs, CRMs, ticketing) safely to internal AI tools
- API-to-MCP conversion: turn existing API documentation into well-designed AI tools
- Shared tool layer powering the in-app assistant, external MCP and internal agents
- Built in: authentication (OAuth/SSO), per-tenant and per-user permissions, rate limits, audit logs
05
Knowledge & Retrieval Systems (RAG)
AI that answers from the business's own documents and data, with sources.
- Search and Q&A over policies, contracts, manuals, tickets and knowledge bases
- Fusion of structured and unstructured data — e.g. ERP records alongside PDFs
- Citations back to the source, so answers can be checked
- Freshness pipelines, so answers reflect current documents
06
Multi-Agent Systems
Several specialised agents working together under an orchestrator.
- An orchestrator plans the work and delegates to specialists (research, analysis, writing, verification)
- A verification step checks results before they reach a user
- Used for complex work: requirements → user stories → tickets, research reports, due diligence
07
AI Inside Existing Products
Add AI features to an existing application without a rewrite.
- Smart search, auto-fill and summarisation
- Classification, recommendations and natural-language reporting
- Cost and latency engineering, so features stay fast and affordable at scale
08
AI Governance, Evaluation & Safety
The controls that make agents trustworthy enough to run in production.
- Evaluation suites that measure accuracy and consistency before and after every change
- Guardrails against prompt injection, data leakage and harmful output
- Human-in-the-loop design for decisions that affect people
- Jurisdiction-aware compliance (India DPDP, EU/UK, US) as configuration and feature flags
- Responsible AI documentation for customers and regulators
Build Approaches
The Approach That Fits You
We choose based on the client's goals, data sensitivity, budget and scale — not on what we prefer to build.
| Approach | What it means | Best when | Trade-offs |
|---|---|---|---|
| Platform-based | Built on hosted models and agent tooling from providers such as Anthropic (Claude), OpenAI, xAI (Grok) or Google (Gemini) | Fastest time to value; best general reasoning; varied tasks | Usage-based cost; data leaves the client's environment (under contract) |
| In-house agents | Agents run on open-weight models (e.g. Llama, Mistral, Qwen, gpt-oss), hosted by the client or on fast inference providers such as Groq | Strict data residency; high volume; fixed narrow goals; cost control | More engineering and operations effort |
| Hybrid | Open-weight models for routine, high-volume steps; frontier models for complex reasoning | Most production systems | More moving parts to manage |
How We Pick the Model
- Start with the task, not the model
- Use the smallest model that reliably meets the bar, measured on the client's own examples
- Keep the design model-agnostic, so providers can be switched as prices and capabilities change
- Fine-tune only when evaluation shows prompting and retrieval are not enough
Production Principles
How We Build Agents That Work
These principles come from running agentic systems in our own products.
Deterministic tools beat free-form generation
Where correctness matters, the agent calls well-defined tools rather than writing raw queries.
The model doesn't need all the data
Give it samples, counts and schemas; let code process and render the full results — more accurate, cheaper and more private.
Lean instructions
Short, consistent prompts with no hardcoded logic. Bloated prompts produce inconsistent agents.
One clear agent loop
Avoid fragile routing layers that send messages down different paths.
Treat untrusted input as untrusted
Documents, emails and uploads may carry prompt injection. Extract structured fields first, then reason over them.
Humans approve consequential actions
Anything irreversible, financial or affecting a person needs a person to approve it.
Detection vs prevention
Agents report problems; system controls (permissions, validations, protected workflows) prevent them. We design both.
Honest reporting
An agent must say "I couldn't check this" rather than produce a confident wrong answer.
Structured output, measured everything
Results render as UI blocks (tables, cards, charts), and every agent ships with evaluations and business metrics.
How an Engagement Runs
From Diagnosis to Live Agents
-
Diagnose
Map the process, people, systems and data. Find where an agent creates real value, and where it doesn't. Output: opportunity map with ROI estimates.
-
Design
Choose the approach (platform / in-house / hybrid), tools, guardrails, human checkpoints and success metrics. Output: solution design and roadmap.
-
Prototype
Build a working version on real (or realistic) data and demo it. Output: working prototype; first meaningful outcome within two weeks.
-
Build
Harden for production: integrations, permissions, evaluations, monitoring, error handling. Output: production system.
-
Deploy
Shadow mode first, then supervised, then autonomous within guardrails. Output: live agent with metrics.
-
Operate & Improve
Monitor accuracy, cost and outcomes; tune and extend. Output: ongoing reports and improvements.
Use Cases by Function
Agents Across the Business
| Function | Example agents and workflows |
|---|---|
| Operations | Process monitoring, exception handling, spreadsheet-to-system automation, order/fulfilment reconciliation |
| Sales & Marketing | Lead research and qualification, personalised outreach with approval, campaign performance analysis, AI-search visibility measurement |
| HR & Talent | Candidate screening support, interview scheduling, onboarding assistants, policy Q&A |
| Finance | Invoice extraction and matching, expense categorisation, variance explanations |
| Customer Support | Ticket triage, draft replies grounded in the knowledge base, escalation routing |
| Engineering & Delivery | Requirements → user stories, release-readiness checks, tracker ↔ repository reconciliation |
| Leadership | Natural-language reporting over company data, scheduled briefings |
| Compliance | Policy monitoring, evidence collection, regulatory change tracking |
Industries
Where We Work
Retail & e-commerce
Marketing & affiliate
Talent acquisition & HR tech
Fleet, rental & industrial operations
Commercial real estate & financial services
Manufacturing & supply chain
SaaS & startups
Private equity portfolio companies
Track Record
Built and Running in Production
Agentic assistants, multi-agent systems and custom MCP servers built and running in production in Genesis's own SaaS platform.
A RAG-based monitoring and matching system for a client — it watches target websites, maps changes against requirements and fills records the client had maintained by hand for years.
Internal delivery agents that reconcile project tracking against code repositories and assess release readiness.
Leadership experience delivering technology at enterprise scale — Honeywell, Sunbelt Rentals, NorthMarq, Symantec and Microsoft.
Engagement Models
Ways to Work With Us
AI Strategy Sprint
Fixed fee. Find and prioritise agent opportunities; produce a roadmap.
Prototype / MVP Build
Fixed fee. Prove one agent or workflow end to end on real data.
Production Build
Project-based. Build, integrate, harden and deploy.
Managed Agents
Monthly retainer. We operate, monitor and improve the agents.
Fractional AI/CTO Leadership
Retainer. Ongoing AI direction, architecture and team guidance.
What Clients Receive
- Working, deployed agents or assistants
- Source code and infrastructure-as-code, as agreed
- Architecture and solution documentation
- Evaluation suite and quality benchmarks
- Runbooks, monitoring dashboards and cost reports
- Team training and handover
FAQ
Common Questions
Whichever fits the task. We work with Anthropic, OpenAI, xAI, Google and
open-weight models, and keep designs model-agnostic.
Yes. In-house agents on open-weight models can run in your cloud or on
your infrastructure.
Not by us. With platform providers, we use enterprise terms under which
your data is not used for training.
Only within limits you set. Agents start in shadow mode, and irreversible
actions need human approval.
It makes your product or systems usable by AI assistants such as Claude
and ChatGPT. Your customers or staff can then work with your software through AI, securely.
Most engagements deliver a first meaningful outcome within two weeks.
Let's Find Where Agents Fit
Start with an honest diagnosis — where agentic AI creates value, what to build first, and a clear-eyed read on fit. No selling what you do not need.