Turn agentic AI from slideware into deployable business infrastructure.
A follow-up artefact for Devika: the concrete fit between Devika’s founder/product engine and 1AI’s working agent stack across multi-agent operations, automation, research, delivery, monitoring, and venture acceleration.
The thesis
AI agents are not just another feature layer. They change how services are delivered, how teams coordinate, how product discovery is run, and how knowledge work gets priced. Devika can be one of the first regional firms to package this as a practical operating advantage.
Discovery, prototypes, specs, tests, docs, QA, research, and delivery support.
Ops monitors, client reporting, research loops, delivery coordination, and engineering support.
Agentic systems for founders, SMEs, corporate innovation teams, and portfolio companies.
Where it fits Devika.
A practical map from Ken’s follow-up to pilot-shaped opportunities, not another “AI transformation” pamphlet wearing a cheap suit.
Use agents to compress discovery, research, technical due diligence, prototype scaffolding, acceptance criteria, QA, docs, and support handoffs.
Build reusable agents around client reporting, project risk scans, meeting-to-spec conversion, backlog grooming, test coverage, and delivery observability.
Package repeatable workflows as a founder-facing AI systems offering: design, deploy, operate, monitor, and improve agentic business infrastructure.
Position Devika as a practical centre of gravity for agentic AI: workshops, live demos, deployable systems, and founder education.
Use agents for venture evaluation, market maps, research synthesis, build validation, KPI monitoring, and ongoing portfolio support.
A concrete next step.
Start small enough to ship, broad enough to reveal the real business surface area.
30-day Devika agent pilot
- Week 1 — workflow selectionPick 2–3 high-friction Devika workflows: founder discovery, delivery reporting, QA/support, or technical due diligence.
- Week 2 — agent design + guardrailsDefine inputs, tools, outputs, approval boundaries, audit trail, security posture, and success metrics.
- Week 3 — working prototypeDeploy the first agent workflow against real internal material and measure cycle-time reduction and quality lift.
- Week 4 — package the offerTurn learnings into a Devika-ready playbook: internal leverage, client-facing workshop, or portfolio acceleration service.
“The opportunity is not AI as a feature. It is AI as a delivery model.”
Devika already has founder trust, product delivery muscle, and regional credibility. The missing piece is a working agentic operating layer that can be shown, measured, and repeated.
Workplace law: a concrete pilot target.
One lawyer, multiple matters, tight deadlines. Here is how the agentic platform shows up in a real practice.
The problem
Workplace lawyers lose hours to repetitive intake, document chasing, and drafting from scratch. The result is slower responses, fewer clients served, and files that stall between meetings.
The platform
A single operating layer that handles intake, case assembly, first-draft output, and matter tracking. The lawyer stays in control; the system removes the friction around the judgment.
Guided intake and triage
Enquiries arrive by email, form, or referral. The platform runs a structured intake, checks completeness, classifies the matter, and drafts missing questions so the lawyer sees a complete file first.
Document assembly and chronology
Uploads are summarised into a live evidence list. The platform assembles a chronology, flags missing items, and sends focused follow-up requests instead of vague reminders.
Drafting and client updates
From the live case summary, the platform drafts response letters, short advice notes, and status updates. The lawyer edits, approves, and applies judgment before anything leaves the practice.
Matter dashboard and tracking
A single view of all active files with stage labels, progress bars, and next-action prompts. The lawyer knows what needs attention without rebuilding the state of every matter from memory.
Matter types that fit first
| Matter type | Why it fits the platform first |
|---|---|
| Unfair dismissal | High volume, fact-calculation-driven, and suited to structured fact gathering. |
| Bullying or harassment | Chronology, correspondence, and escalation tracking benefit from a case summary layer. |
| Contracts and advice | Recurs often, has clear templates, and needs fast first-draft turnaround. |
| Response letters | Deadline pressure makes drafting support and task automation immediately valuable. |
Start with a pilot that proves capacity.
Discovery and workflow mapping
- Map the top 3 matter types and the repetitive steps inside each one.
- Define what must stay human reviewed and what can be drafted or routed by the platform.
- Set the metrics: response time, drafting time, matter throughput, and admin hours saved.
Pilot build and testing
- Launch intake forms, draft templates, matter dashboards, and a document summary workflow.
- Test on live matters with lawyer sign-off before any client-facing output goes out.
- Refine prompts, templates, and safeguards based on real file behavior.
Scale and standardise
- Roll the winning workflows across additional matter types.
- Build a knowledge base of clauses, letters, and response patterns.
- Introduce ongoing reporting so the lawyer can see capacity gains month by month.
Expected value
Less context switching, faster drafting, fewer repetitive admin tasks, and better control across multiple matters at once. That usually means more clients handled without the practice feeling fragmented.
2. A 30-day pilot for the highest-volume matter types.
3. A follow-on rollout once the lawyer sees working output and metrics.
This keeps the purchase decision practical: prove value on a few workflows, then expand.