Tech Stack
— What we build —
Our AI services. One approach.
AI Agent Development
Custom multi-agent systems, deployed in production. Not demos.
- Tool use, planning, memory
- Eval pipelines + observability
- Production deployment + handoff
AI Chatbot Development
LLM-powered chatbots and assistants wired into your data and tools.
- RAG over your knowledge base
- Multi-channel (web, WhatsApp, helpdesk)
- Guardrails + human handoff
AI Implementation
Ship AI into your existing stack — CRM, helpdesk, scheduling, internal apps.
- OpenAI / Claude API integration
- Workflow automation in real ops
- Auth, security, deployment
AI Workflow Automation
Automate the manual busywork between your systems, end-to-end.
- Cross-tool process automation
- Document + data pipelines
- Human-in-the-loop approvals
AI Consulting
Strategy and architecture without the $40K deck. For teams figuring out where AI fits.
- AI readiness review
- Build-vs-buy decisions
- Architecture review
— Proof, not promises —
Shipped to production, not slideware.
“Pankaj and the Metageeks team went from brief to working AI chatbot in under three weeks. What would have taken us 4 months with a new hire, they shipped in 21 days — production-ready, properly tested.”
— Selected work —
From brief to production.
— How we choose the stack —
Picked for fit, not familiarity.
Framework-agnostic by default. Here's the layer-by-layer reasoning behind every build.
TypeScript-first; framework chosen per project.
Benchmarked per task, often both behind a router.
Eval coverage + traces before any user sees it.
Boring, durable, hire-able — nothing exotic to maintain.
ECS / Lambda, RDS, VPC isolation, least-privilege IAM.
RLS, audit logs, least-privilege by default.
— How we work —
From audit to production, in five phases.
30-page report identifying where AI is worth building. Or a discovery call if scope is already clear.
Fixed-fee SOW or T&M estimate. We name the milestones, you sign once.
Weekly demos, eval coverage on every agent, no surprise integrations.
Deployed to your infra or ours. Observability + alerts wired in before launch.
Docs, runbooks, on-call optional. Most teams take it in-house here.
— Three ways to engage —
Pick the entry that fits your scope.
Audit
$497For teams not sure where AI fits yet.
- Output
- 30-page report
- Timeline
- 7 days
- Investment
- $497 fixed
Fixed-fee build
SOWFor teams with a clear scope ready to ship.
- Output
- Production software
- Timeline
- 4–12 weeks
- Investment
- Fixed after discovery
Retainer / T&M
MonthlyFor multi-workstream or evolving roadmaps.
- Output
- Embedded senior team
- Timeline
- Month-by-month
- Investment
- Capped monthly
— Not sure where AI fits? —
Start with the $497 AI Profit Leak Audit.
A 30-page report identifying prioritized AI automations and profit leaks across your operations. 7-day delivery. A specific, affordable first step — instead of a $40K strategy engagement.
— Who you'll work with —
Run by the founder, not a sales team.
Pankaj founded Metageeks in 2022 to build fixed-price AI products for small and mid-sized businesses. He works directly with customers from Delhi, India. No account managers, no offshore handoff — you work directly with the person who scopes and ships your build.
— From the blog —
Field notes on building with AI.
— Real questions —
What we get asked.
Why $497 for an audit when others charge $40K?
Because we don't need to. The audit is a productized 30-page report — same methodology each time, run by a senior team, delivered in 7 days. The $40K version exists for a reason: it includes 6 weeks of consultant time. Ours doesn't. If your problem warrants 6 weeks of consultant time, the audit will tell you so honestly — and you'll spend the $40K with someone else, with eyes open.
What if we already have engineers?
Most of our build engagements are with teams that have engineers — we just have specific AI/agent expertise they don't. We're happy to scope alongside an internal team, transfer ownership early, or run as a short-term capability boost. The audit also works as a 'where to point our own engineers' exercise.
How long does a typical build take?
A first agent in production is usually 4–8 weeks. A multi-agent workflow is 8–12. We don't quote anything sight-unseen — every build starts with discovery (or the audit) so the timeline reflects your actual stack and ops, not a template.
Are you locked into a specific AI framework or stack?
No. We select frameworks and tools based on your existing infrastructure, team's skill set, and what will be easiest to maintain after handoff. Our engineers have shipped with multiple agent frameworks and LLM providers. We're not resellers or partners — we have no financial incentive to push any particular tool.
Do you sign NDAs / DPAs?
Yes. Every engagement starts with an MSA + SOW. NDAs before audits if requested. DPAs for any work touching customer data. We're a real company, not freelancers — boring paperwork is part of the job.
What if the audit recommendations don't fit our budget?
Then you don't build them, and you spent $497 to find that out instead of $40K. The audit ranks recommendations by ROI and effort — most teams find at least one play that pays back inside a quarter. If none of them do, that's a real result too.
Can we hire you for just consulting, no build?
Yes — that's the AI Consulting pillar. Strategy, architecture review, build-vs-buy decisions, AI readiness review. Hourly or fixed-scope. No sales pressure to convert into a build engagement; sometimes 'don't build this' is the answer.
Fixed price. Defined outcome. Or you don't pay for the miss.
Every engagement is scoped to a named deliverable at a fixed fee, quoted once. If we miss the spec we agreed, we make it right — no hourly surprises, no abandoned half-builds.