InsightSolTechnologies
Verified engineering deployments

Three systems, three architectures, measured results.

Each case study follows the same structure: the operational problem as the client experienced it, the system we designed, the architecture it runs on, the results measured in production, and the engineering lessons that carried into later work. Where a number appears, it was measured on the deployed system, not projected.

The three systems span outbound sales orchestration, geospatial computation, and field-services intake. Each has its own page with the full breakdown.

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Case Study 01 · RingLeadAI

AI Outbound Engine and Multi-Agent SDR Orchestration

Sales automation platform

A multi-agent outbound engine that researches, qualifies, personalizes, and books, with human sales reps reviewing exceptions, not running the process.

  • Complex transactions, from inbound reply through research, qualification, and booking, complete in under 80 seconds end to end
  • Human SDRs review exceptions and escalations only; the routine loop runs unattended
  • Every sent message carries a citation trail, so hallucinated company facts are blocked before sending rather than discovered by a prospect
PythonFastAPIClaude APIOpenAI APIPostgreSQLRedis

Headline result

<80s

from inbound signal to a verified, booked outcome

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Case Study 02 · DirtBid

Real-Time Geospatial and Earthwork Estimation Pipeline

Construction bidding and earthwork

An event-driven pipeline that ingests site plans and geospatial data, computes cut-and-fill volumes, and produces traceable, bid-ready estimates.

  • 99.4 percent pipeline uptime measured across a full production quarter, including bid-deadline peaks
  • Every accepted input is accounted for by a reconciliation job: an estimate or an explained, replayable failure
  • Estimators inspect the surfaces and volumes behind any number, replacing spreadsheet transcription with traceable computation
PythonPostgreSQLPostGISRedisDockerKubernetes

Headline result

99.4%

pipeline uptime across a full production quarter

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Case Study 03 · Field-services operator

Enterprise Automated Lead Triage and Voice Dispatch Architecture

HVAC and field services, 60-plus technicians

A voice and chat intake layer, automated triage engine, and dispatch integration that answers, prioritizes, and books every inbound request on its own.

  • 42 percent reduction in operational triage time, with requests classified and routed before a dispatcher opens them
  • 38 percent more jobs booked from the same call volume
  • 62 percent of routine calls handled end to end without office staff
PythonTwilioClaude APIOpenAI APIPostgreSQLRedis

Headline result

42%

reduction in operational triage time

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What the three have in common

Different industries, the same engineering pattern.

An outbound sales engine, an earthwork estimation pipeline, and a field-services intake system look nothing alike from the outside. Underneath, they share the four decisions that determine whether a system survives contact with production volume. Those decisions are made in the architecture phase of every engagement, and they are why the numbers on these pages held up after launch rather than only on demo day.

Each detail page records the challenge as the client experienced it, the system we designed, the components and request flow, the results measured in production, and the engineering lessons we carried into later work. Read them in any order; the pattern is the same, and the numbers are specific to the deployment they came from.

01

Typed boundaries

Every input is validated against a schema at the edge, and every tool call is checked on the way in and the way out.

02

Durable state

Work is persisted at each committed step, so a crash or provider outage resumes rather than restarts.

03

Explicit failure handling

Retries, dead-letter queues, idempotency keys, and human escalation are designed before launch, not after the first incident.

04

Telemetry on every request

Traces, cost per transaction, and evaluation pass rates are recorded from day one, so results are measured rather than estimated.

Want the architecture behind your own numbers?

The audit produces the same diagrams and unit economics these systems started with, for your workflow and your data.

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  • Read-only access under NDA
  • Written review and diagrams
  • Fixed-price roadmap