What problems does Gemini typically solve first?

High-value knowledge work: search across private corpora, assisted drafting, triage and routing, forecasting from operational data, and agent workflows that reduce repetitive handoffs — always with measurable quality gates.

Which model providers and stacks do you support?

OpenAI, Anthropic, Google, and open-weight models (Llama, Mistral, and peers) behind a routing layer. Orchestration commonly uses Python/TypeScript with LangChain or custom graphs, Postgres/pgvector or managed vector stores, and OpenTelemetry for traces.

How do you keep AI systems safe and auditable?

Guardrails on inputs and outputs, ACL-aware retrieval, offline and online evaluations, red-team scenarios for critical paths, and human approval for irreversible actions. Every production path is traceable.

How long to a production-ready MVP?

Discovery is typically 2–3 weeks. A scoped MVP with evals and monitoring usually ships in 6–10 weeks, then we harden cost, latency, and coverage in continuous waves.

Will our data leave our estate?

We design for your constraint: private VPC endpoints, self-hosted models, or approved SaaS with contractual controls. Data residency and retention are decided before the first training or retrieval pipeline lands.

Satyakabir Technologies

Technology engineering · Digital transformation

Technologies · Gemini

Gemini

Multimodal Google AI model stacks.

Modern, battle-tested technology stacks
Fast loading speeds and mobile-first design
Pre-integrated Indian UPI and payment gateways

<200ms

p95 target

Eval-gated

Releases

HITL

Critical paths

Traceable

Every token path

LLM platformsRAGAgentsEvalsGuardrailsMLOpsFine-tuningHITL

01 · Problem

Why most AI programs stall

Gemini fails quietly when demos never become durable systems. Notebooks hide latency, cost, and safety risk. Satyakabir designs AI as a product surface: retrieval with access control, agents with approval gates, and evaluation that blocks unsafe releases before users ever see them.

02 · Capability

What we actually build

LLM platforms, RAG knowledge layers, generative assistants, computer vision pipelines, classical ML for forecasting and classification, and agentic workflows that call tools under policy. Each system ships with tracing, cost ceilings, and human-in-the-loop paths for high-stakes decisions.

03 · Approach

How delivery works

Multimodal Google AI model stacks.

04 · Outcome

What your team owns

You leave with a running system, an eval harness your engineers can extend, runbooks for incident response, and operators who understand every token path — not a black-box vendor dependency.

AI engagement

Ready to put intelligence into production?

Book an AI strategy session with a principal. We leave with a scoped first slice, eval plan, and clear ownership model.

Gemini

Production AI — evaluated, governed, and observed from the first release.

Why most AI programs stall

What we actually build

How delivery works

What your team owns

Ready to put intelligence into production?

Gemini

Production AI — evaluated, governed, and observed from the first release.

Why most AI programs stall

What we actually build

How delivery works

What your team owns

Ready to put intelligence into production?