Agentic AI consulting for teams that ship.Architecture, implementation, observability, and handoff — not advice.
Most agentic AI engagements end with a PowerPoint deck. Ours end with production code your team owns: an agent system architected for your operations, built on Claude API and MCP, instrumented with Langfuse-grade observability, and handed off with the documentation and evals that let your team maintain and extend it.
Replace manual ticket routing, document classification, and L1 support with agents that read your operational systems and escalate intelligently. Typical payback: 2–4× throughput per reviewer.
PR review + CI agent pipelines
Parallel fan-out of code-review, security-scan, and test-generation agents triggered on every PR open. Senior engineers stop being the first-pass reviewer.
ERP integration agents
MCP servers wrapping Odoo / SAP / NetSuite endpoints, behind a supervisor agent that handles customer queries with full ERP context. Read-only by default; mutating actions guarded.
Content + SEO pipelines
Research → outline → draft → review pipelines for teams producing content at scale. Built with the same patterns used for code-review pipelines: parallel where independent, supervised on the write turn.
Compliance + document review
Single-agent systems with multi-layer guardrails for regulated workflows: PII redaction, versioned policy registry, output validation, human-in-the-loop gating above risk threshold.
Multi-agent automation platforms
For teams that already have one agent in production and want a portfolio — shared MCP servers, shared observability, shared eval infrastructure, supervisor-pattern orchestration across agents.
Industries served
IT ServicesERP / Enterprise SoftwareFinancial ServicesHealthcare OperationsEdTechReal Estate Tech
System architecture
How the system is wired
Standard production agent architecture
Technology
Technology stack we deliver on
Methodology
Delivery methodology
01
Discovery
One week. Understand your operations, the workflow we are automating, the team that maintains it, and the constraints (compliance, latency, cost). Output: a scoping document and a go/no-go on the engagement.
02
Architecture
1–2 weeks. Full architecture document covering agent role(s), tool registry, MCP servers, orchestration pattern, memory model, observability plan, and eval-set seed. Reviewed with your engineering leads before any code is written.
03
Implementation
3–5 weeks. Production-grade code built on your stack. Daily commits to a branch your team can audit. Working agents on a staging environment from week 2.
04
Eval + observability
1 week, parallel to implementation. Eval set with adversarial cases, Langfuse traces wired through every step, cost attribution per agent, alert thresholds defined and validated.
05
Handoff
1 week. Documentation, a maintenance playbook, the eval-set update process, and a structured walkthrough with your team. Optional 30/60/90-day check-ins.
Security & scalability
Security & scalability considerations
Least-privilege tool design
Every tool is scoped to the minimum permissions it needs. Mutating actions require an explicit confirmation step or run through an approval queue. Read-only by default; we add write permissions deliberately and audit each addition.
PII + secrets handling
Input filters strip or redact PII before reaching the reasoning model where the workflow allows it. Secrets never enter prompts — they live in your secret manager and are referenced by tools.
Audit log + decision trace
Every agent decision logs: inputs, the tools called, the model used, the cost, the score, the final output. Replayable. Reviewable. Auditable.
Scale primitives
Prompt caching for stable prefixes, parallel sub-agent execution for independent reads, queue-based fan-out for high-throughput workflows. Designed for 10× current load without re-architecture.
Cost discipline
Cache-hit-rate, tokens-per-completed-task, and cost-per-decision tracked from day one. Most engagements ship 30–60% cheaper than the naïve agent baseline they replace.
Rollback + circuit breakers
Every production agent has a kill switch and a fallback path. We define failure modes explicitly during architecture and rehearse them before go-live.
Integrations
Integration capabilities
Odoo · SAP · NetSuite · Microsoft Dynamics
GitHub · GitLab · Bitbucket · Linear · Jira
Salesforce · HubSpot · Zoho · Pipedrive
Slack · Microsoft Teams · WhatsApp Business
PostgreSQL · MySQL · MongoDB · Snowflake · BigQuery
We measure outcomes against the baseline workflow we are replacing. Typical numbers from recent engagements:
2–4×
throughput per reviewer / operator
6–10 wk
time-to-production for the first agent
< $5
average cost per agent decision in steady state
100%
of code and IP owned by you
Case studies
How recent engagements actually shipped
IT Services · 6 weeks discovery → handoff
PR review pipeline cuts senior-engineer time 4×
Mid-market IT services firm · Ahmedabad · 180 engineers
Problem
Senior engineers were spending 8–12 hours per week each on first-pass PR review across a 6-team monorepo. Junior PRs waited 2+ days for sign-off; velocity stalled; the highest-judgement people were doing the lowest-judgement work.
Solution
A multi-agent CI workflow triggered on every PR open. Three specialist agents run in parallel — a reviewer (Claude Sonnet 4.6) for code-correctness and convention, a security agent for risk patterns, and a test-generator agent for coverage gaps. Outputs are consolidated into a single PR comment within 90 seconds. Humans review the agent's synthesis, not the raw diff.
Claude Sonnet 4.6 (reviewCustom MCP server: GitHub APIGitHub ActionsLangfuse traces
~36 hrs/wk
senior engineer time reclaimed across the team
< 3 days
payback period at loaded-cost rate
4×
review throughput per senior engineer
0
production regressions traced to AI-passed reviews in 90 days
Customer support backlog had grown to ~340 open tickets. Level-1 triage took 12–20 minutes per ticket on average, and 35% of tickets were misrouted on first pass — every misroute became a customer-facing escalation churn.
Solution
A supervisor-pattern agent that ingests email and form submissions, classifies the issue, queries the customer's Odoo instance for context (open invoices, recent modules, last login, current contracts), drafts a Level-1 response with the right module screenshots inline, and routes complex tickets to the right consultant with a pre-filled handoff brief.
Claude Sonnet 4.6 (drafting)Custom MCP server: Odoo (read-only customer / order / invoice scope)Supervisor patternPydantic schemas
340 → 18
open L1 backlog within 6 weeks of go-live
~60%
L1 staffing reduction on agent-eligible categories
Audit-grade compliance review ships under multi-layer guardrails
Regulated financial-services intermediary · India · 95 employees
Problem
Manual compliance review of vendor and onboarding documents was the bottleneck for new-customer activation. Every traffic spike threatened SLA breach. Reviewer fatigue led to inconsistent flagging — some weeks too strict, some weeks too loose, with no defensible pattern.
Solution
A single-agent system wrapped in four guardrail layers: an input filter that detects and redacts PII / strips prompt-injection patterns; a versioned policy registry the agent must cite by clause ID for every conclusion; output validators (schema + LLM-as-judge cross-check); and a human-in-the-loop gate on anything scored above a defined risk threshold. Every decision is appended to an immutable audit log.
Custom detectorsClaude Opus 4.7 (final ruling)Versioned in repoPydantic v2
Workshop / Public Build · 1 day · 8 hours hands-on
The Agentic Operating System — workshop build
AIMED · public workshop · ~40 engineers
Problem
Most teams meeting agentic AI for the first time get stuck on one of three blockers: tool design, orchestration choice, and the gap between a working demo and a system that survives Monday morning. The AIMED workshop format compresses the answers into one day of hands-on building.
Solution
A day-long live build of "the Agentic Operating System" — a multi-agent shell with a supervisor (planning, decomposition), handoff agents (parallel reads, sequenced writes), shared tool registry via MCP, and observability wired in from line one. Every attendee leaves with a running shell on their own laptop, the source, and the patterns to extend it.
Claude Sonnet 4.6 + Haiku 4.5 (free Claude Code tier worked)Three MCP servers built from scratch: filesPython supervisor + handoff context passingLangfuse traces from the first agent call
40
engineers shipped a running multi-agent shell on their own laptops
Multi-agent research synthesis — open PoC for swarm vs supervisor
Public R&D · open-source on GitHub
Problem
Every team building multi-agent systems faces the same orchestration question and answers it from intuition, not measurement. "Supervisor is cleaner" vs "swarm is faster" gets stated as fact in a hundred conference talks without a single side-by-side benchmark anyone can reproduce. This PoC builds and measures both, on a task with a defensible ground truth.
Solution
A reproducible benchmark: same task (synthesise a literature review across 12 papers on a given topic), same model, same MCP tool registry, same eval rubric. Three runners — single-agent (baseline), supervisor pattern, swarm pattern — each scored on factuality, citation accuracy, coverage, and cost. Code + eval data + raw runs all open-sourced.
Claude Sonnet 4.6 (all three runners use the same model)Custom MCP servers: paper-fetchThree parallel implementations sharing the same tool registryOpen eval rubric