Support01
Customer support agents
Resolve tier-1 tickets across email, chat and WhatsApp — escalate cleanly when they should.
Agentic AI development
Production-grade evalsHuman-in-the-loop by defaultPII-safe executionYou own the code
Evals
Built in from day one
Evals
Built in from day one
HITL
Human-in-the-loop by default
5.0
Google rating
You own
The spec and the code
What we build
Sales02
Outbound & SDR agents
Research accounts, draft personalised outreach and book meetings — under your sales playbook.
Research03
Research assistants
Brief generation, market scans, competitor monitoring — with cited sources you can audit.
Ops04
Internal ops agents
Triage tickets, classify documents, update CRM records — quietly running on your back-office work.
Engineering05
Code & PR agents
Open scoped pull requests, run on-call playbooks, monitor flaky tests — alongside your engineers.
Knowledge06
Internal knowledge agents
Surface the right doc, ticket or decision — across Notion, Linear, Drive and your data warehouse.
Anatomy
01
Planner
An LLM reasoning loop that decomposes the goal into steps — and can backtrack when a step fails.
- Goal decomposition
- Reflection on failures
- Bounded autonomy
02
Tools
Typed tool calls into your APIs, SaaS apps and internal services — sandboxed, rate-limited, audited.
- Typed JSON schemas
- Rate-limit + retries
- Per-tool audit trail
03
Memory
Short-term context plus long-term vector store, scoped per tenant and per user with redaction policies.
- Per-tenant isolation
- Vector + keyword recall
- Redaction & retention
04
Evals
Offline golden sets and live production evals, so changes ship behind measurable gates — not vibes.
- Golden eval sets
- Live regression dashboards
- Cost / latency budgets
What you get
8 items, none of them extra
Custom agent built against a written specification you own
Typed tool schemas + auditable run logs for every step
Golden eval set + live production regression dashboards
Human-in-the-loop UI for any high-stakes action
Cost, latency and token-budget dashboards in Looker Studio
PII redaction + per-tenant memory isolation by default
Slack / Linear / email notification routing
30-day post-launch tuning window — prompt and eval iteration
How it runs
- 01Week 1
Workflow discovery
We sit with the team, watch the work, and pick the loop where an agent will actually compound.
Workflow map · Use-case scoring
- 02Week 2
Tool & data design
Tool surfaces, schemas and the memory model — what the agent can touch and what it absolutely cannot.
Tool schemas · Memory plan
- 03Week 2
Eval suite first
Before the agent runs in anger, we build the golden set. Changes ship behind measurable gates from day one.
Golden set · Eval harness
- 04Weeks 3–6
Agent build
Planner + tools + memory wired with bounded autonomy. Human-in-the-loop where stakes are high.
Releasable agent · HITL UI
- 05Week 7
Shadow mode
The agent runs alongside humans on real workload. We measure agreement, cost and latency before flipping the switch.
Shadow-mode report
- 06Ongoing
Production & retro
Staged rollout behind flags. Live evals, cost dashboards and a quarterly retro on what the agent should learn next.
Live dashboards · Retro
The stack
Anthropic ClaudeReasoning
OpenAIReasoning
LangChainOrchestration
Vercel AI SDKRuntime
PythonBackend
SupabaseVectors + state
SlackSurface
LinearWorkflow
FAQ
- 01How is an AI agent different from a chatbot?
- A chatbot answers. An agent does. It plans a goal into steps, calls real tools in your stack (CRM, ticketing, internal APIs), reasons over the result, and either finishes the task or hands off to a human. The work, not just the conversation.
- 02What does "production-grade" mean for you?
- Evaluated. Bounded. Observed. Every agent ships behind a golden eval set, sandboxed tool execution, audit trails on every step, and dashboards for cost, latency and task-completion. Changes pass evals before they roll out.
- 03How do you keep our data safe?
- Per-tenant memory isolation, configurable PII redaction, scoped API keys for every tool call, and the option to host on your cloud (AWS / GCP / Azure). We sign DPAs on request and align to your data-residency rules.
- 04How long does an agent take to ship?
- A focused single-workflow agent ships in 4–6 weeks including evals and shadow-mode. A multi-tool agent across several systems usually takes 8–12 weeks. We give a realistic estimate after the workflow-discovery call.
AI Development across Delhi NCR
AI Development company in DelhiWe work with businesses across Delhi. Each area page covers the local business context, the problems worth solving there, and how a ai development project is typically scoped.
AI Development across United States
AI Development company in United StatesWe work with businesses across United States. Each area page covers the local business context, the problems worth solving there, and how a ai development project is typically scoped.
