Engineering Plan

The technical roadmap, from a functional MVP to the intelligence layer for customer operations.

How we build OpsPulse AI in six phases: unify the data, make it intelligent, put a copilot on top, then harden and scale. Each phase has a clear goal, what we build, the stack, and the output you can point to.

Done Foundation Intelligence Copilot Pilot & hardening Platform & scale
The architecture in one line

Every phase builds one stage of the same closed loop.

Sources

Tickets · calls · QA · surveys · chat

Signal Aggregator

Ingest · normalize · unify

Intelligence Engine

Patterns · anomalies · root cause

Ops Copilot

Prioritized decision feed

Recommendation

Coach · fix · escalate

Actions write back to the source tools, closing the loop and generating fresh signal.
The differentiating column

External signals: the second inbound edge.

Every source in the pipeline above is a system the customer owns, reached with a credential they give us. External signals are third-party data about the customer's own business — acquisition, distress, stakeholder change, funding — which makes licensing, entity resolution and provenance the hard parts rather than the connector.

Built in Phase 0

The surface is running in the MVP today.

Event schema on the account: type, confidence, effect, detected-at, source
External Signals panel, ranked most recently detected first, behind the entitlement gate
Provenance rendering, with a sample badge and a null source_url on seeded records rather than a fabricated link
Query path: filter the book by external event type
Running on sample fixtures. Four event types, ~2.5% of accounts, deliberately sparse.

Phase 1 · the feed

Swapping fixtures for licensed data. The schema is built so the wiring is a swap.

Licensed feed integration — Crunchbase, Tracxn, ZoomInfo. A procurement decision before it is an engineering one
Entity resolution. Matching a filing about "Northwind Group" to the account "Northwind Ltd" is an inference with a confidence, not a string comparison
Real source URLs, at which point the sample badge disappears on its own

Phase 2 · fusion

Today an event surfaces against the account. It does not yet move the score.

External events change the prior on an internal renewal risk
Explicitly not a second score beside the first — two numbers that can disagree about one account is the health-score failure mode with an extra step
Confidence per event type carries through: an acquisition filing is not a departed VP
The Build

Six phases, ~12 months to production.

Phase 0 · Functional MVP
Prove the loop
Done · Now

Validate the UX and the "signal → decision" story with stakeholders before writing backend code.

What we build
Functional MVP: landing site + interactive Ops Copilot dashboard + analytics view, running a real engine rather than a click-through
Realistic mock data modelling the decision feed, root-cause drill-down and live signals
Stack
HTML / CSSVanilla JSHand-rendered SVG chartsGitHub Pages
Output: a live demo for the pitch and for user-feedback interviews, with no infrastructure required.
Phase 1 · Foundation
Data foundation & Signal Aggregator
Months 1–3

Unify fragmented signals into one trusted, multi-tenant data layer.

What we build
Source connectors: ticketing, telephony, QA, surveys and chat (webhooks + pollers)
External signal feed: licensed M&A, funding, stakeholder-change and distress data, resolved to accounts with a confidence and a real source URL (the surface for it already exists)
Ingestion pipeline → normalization → a unified operational schema
Multi-tenant storage, auth, and an internal API
Stack
Python / FastAPIKafka / RedpandaPostgresSnowflake / BigQuerydbtObject storage
Output: a live, deduplicated, unified dataset per customer, the raw material everything else runs on.
Phase 2 · Intelligence
Intelligence Engine
Months 3–6

Turn raw signals into patterns, anomalies, root causes and risk scores.

What we build
NLP on tickets & transcripts: sentiment, intent, topic clustering, entity extraction
Anomaly & trend detection; risk-scoring models (churn, SLA breach, coaching gap)
External-event fusion: an acquisition or a departed sponsor changes the prior on the renewal risk rather than becoming a second score
LLM reasoning layer (Claude) for root-cause narratives and summaries
Stack
Claude API (Anthropic)Transformers / spaCyEmbeddings + vector DBscikit-learnFeature store
Output: scored, explained risks that update in near real-time as new signal arrives.
Phase 3 · Copilot
Ops Copilot & recommendations
Months 6–9

Deliver the prioritized decision feed and one-click actions in a real product UI.

What we build
Production React dashboard with a real-time decision feed and prioritization/ranking
Recommendation Engine: suggested interventions per risk
Write-back actions (assign, coach, escalate) into the source tools
Stack
Next.js + TypeScriptD3 / RechartsGraphQL / RESTWebSocketsClaude (recommendations)
Output: managers act from one place, and the closed loop from signal to action is complete.
Phase 4 · Pilot
Pilot & enterprise hardening
Months 9–12

Prove value in real environments and make the product secure and reliable.

What we build
Pilot deployments with tight feedback loops and outcome tracking
MLOps: retraining, monitoring, drift detection; predictive/prescriptive analytics
Enterprise readiness: SSO, RBAC, SOC 2, encryption, audit logs
Stack
KubernetesTerraformGitHub Actions CI/CDDatadog / GrafanaOIDC / SAMLMLflow
Output: a reliable, secure, self-improving product with real pilot proof points.
Phase 5 · Scale
Platform & ecosystem
12+ Months

Become the extensible intelligence layer, the long-term vision.

What we build
Integration marketplace and a public API / SDK for third parties
Custom per-customer models and prescriptive automation at scale
Multi-region infrastructure and deep observability
Stack
API gatewayPlugin SDKMulti-region cloudFine-tuned / custom models
Output: an ecosystem and the scale to reach the global vision.
Tech Stack

The stack, by layer.

Frontend

The Ops Copilot dashboard and analytics.

Next.jsTypeScriptReactD3 / Recharts

Backend / API

Services, real-time feed, write-back.

FastAPI / NodeGraphQLWebSockets

Data & Pipeline

The Signal Aggregator and warehouse.

KafkadbtPostgresSnowflake

AI / ML

The Intelligence & Recommendation engines.

Claude (Anthropic)TransformersVector DBscikit-learn

Infra / DevOps

Ship, run and observe reliably.

AWS / GCPDockerKubernetesTerraform

Security

Enterprise-grade from day one.

SSO (OIDC/SAML)RBACSOC 2Encryption
Talking Points

How to explain it in 60 seconds.

1

Start with the loop, not the tools.

"We collect every customer signal, make sense of it, surface the few things that matter, recommend the fix, and push that action back into the tools." The whole product is one closed loop, and each phase builds one stage of it.

2

Data first, intelligence second.

Phase 1 is unglamorous but essential, because you can't be smart about data you haven't unified. We de-risk the hard integration work before layering AI on top.

3

AI is a layer, not the whole thing.

Classic ML handles scoring and anomaly detection; the LLM (Claude) handles reasoning: root-cause narratives and recommendations in plain language. Each is used where it's strongest.

4

Value is provable at every phase.

Every phase ends in a concrete output you can demo: a unified dataset, scored risks, a working copilot, pilot results. Investment and proof move together.

5

~12 months to a production pilot.

Foundation → Intelligence → Copilot → Pilot. Phase 5 is the scale story after product-market fit: the platform and ecosystem that reaches the global vision.

See the roadmap's first phase, already live.

Phase 0 is built. Explore the functional MVP the rest of this plan grows from.

Open the live Copilot → Back to the site