InsightSolTechnologies
System Review and Architecture Consultation

Know exactly what to build, what it will cost to run, and what it will return.

The Architecture Audit and Discovery engagement is a comprehensive, dual-track evaluation. One track is for systems already in production that have hit a wall on latency, cost, reliability, or security. The other is for products that do not exist yet and need a feasibility verdict, a data plan, and an honest unit economics model before anyone commits a budget.

Both tracks end the same way: a written Architecture Review Document, an entity relationship diagram, a data flow diagram, and a fixed-price implementation roadmap you can execute with us or with your own team.

Start the intakeBook a call first

Two tracks, one method

Pick the track that matches where your system is today.

Both tracks use the same evidence-first method: measure before recommending, prove before promising.

Track 1

Scaling an Existing System

For founders and CTOs with working software that is hitting bottlenecks.

Your system works. Customers use it, revenue depends on it, and that is exactly why every change feels risky. Queries that were instant at ten thousand rows now time out, the model bill grows faster than usage, the agent pipeline that handled a hundred cases a day stalls at a thousand, and nobody is sure which fix to make first.

The scaling track treats your production system as evidence. We read the code, profile the database against real query logs, trace a sample of agent runs end to end, and price every model call. The output is a ranked list of bottlenecks with the measured cost of each one and the engineering effort to remove it, so the next quarter of work is chosen by numbers instead of by whoever argues loudest.

What the track covers

  • Code audit

    Structure, boundaries, error handling, and test coverage reviewed against how the system actually fails in production, with concrete refactoring targets.

  • Database slow-query profiling

    Query logs, execution plans, index coverage, and lock contention analyzed to find the handful of queries responsible for most of the latency.

  • Token cost optimization

    Every model call classified by purpose, measured for cost and accuracy, then mapped to a cheaper model, a cache, or a rule where quality allows.

  • Multi-agent latency refactoring

    Agent traces analyzed for serial steps that could run in parallel, redundant tool calls, and oversized context windows that slow every turn.

  • Security review

    Prompt-injection surfaces, secrets handling, access control on tool actions, and data flows to external providers reviewed against your compliance obligations.

Typical symptoms

  • p95 latency climbing month over month with no code change
  • Model spend growing faster than transaction volume
  • Agent runs that succeed in testing and stall under real load
  • A database nobody wants to touch because the last migration hurt
  • Security questions from an enterprise prospect that nobody can answer quickly

Track 2

Engineering a Greenfield Product or New AI Concept

For companies launching new platforms, products, or AI-driven lines of business.

New AI products fail for predictable reasons: the data needed to make the model useful is not actually reachable, the unit economics only work at a volume the business will never reach, or the first version tries to automate the hardest step instead of the most valuable one. All three are visible before a single sprint starts, if someone looks.

The greenfield track is that look. We run a technical feasibility study on the real data sources, draw the data pipeline that would feed the product, select a stack based on your team's ability to operate it, and model the unit economics at launch volume and at scale. The result is an MVP scope that is deliberately small, measurably valuable, and already costed.

What the track covers

  • Technical feasibility study

    Proof calls against your real data to confirm the model can do the job accurately, with measured error rates on representative cases.

  • Data pipeline blueprint

    Sources, ingestion, transformation, storage, and retrieval designed as a diagram with volumes and latency budgets attached.

  • Tech stack selection

    Languages, frameworks, model providers, and infrastructure chosen for the workload and for the team that will own it after launch.

  • Unit economics modeling

    Cost per transaction across model, infrastructure, and human review, projected at launch volume and at ten times launch volume.

  • MVP sprint scoping

    A first release scoped to the smallest system that proves value, with sprint-level estimates and acceptance criteria.

Typical symptoms

  • A board deck that promises AI without a data plan behind it
  • Three vendors quoting three different architectures
  • No agreed definition of what the first version must do
  • Uncertainty about whether the economics work at your price point
  • An existing team that is strong in one stack and nervous about another

How the audit runs

Four phases, two to three weeks, one written verdict.

Access is read-only and under NDA throughout. Nothing is recommended that was not measured first.

  1. 01 · Discovery

    Access, interviews, and the baseline

    Read-only access to repositories, databases, and logs is set up under NDA. We interview the people who operate the system or own the idea, and record the baseline: current cost per transaction, latency, failure rate, and the business number the work must move.

  2. 02 · Analysis

    Profiling, tracing, and proof calls

    Queries are profiled against real logs, agent runs are traced end to end, and proof calls are run against representative records. For greenfield work this is where feasibility is proven or disproven with measured error rates, not opinions.

  3. 03 · Design

    Diagrams, options, and economics

    Findings become an entity relationship diagram, a data flow diagram, and a set of architecture options, each priced for build effort and run cost. Unit economics are modeled for the recommended option at current and future volume.

  4. 04 · Roadmap

    Written review and fixed-price plan

    Everything is written up in the Architecture Review Document and walked through live with your team. The implementation roadmap is fixed-price, sequenced by return, and usable by any engineering team, ours or yours.

What you receive

Deliverables an engineering team can act on the same day.

Every deliverable is written to stand on its own. The review document is readable by a CEO and actionable by a CTO. The diagrams are the ones your engineers would have drawn if they had three weeks with nothing else to do. The roadmap has a price on every phase, so the next decision is a budget decision, not another round of discovery.

12-page Architecture Review Document

A written review covering current state, measured bottlenecks or feasibility findings, recommended architecture, security posture, and the unit economics model. Written for a CTO to act on and a CEO to read.

Entity Relationship Diagram (ERD)

Every entity the system stores or will store, with relationships, ownership, tenancy boundaries, and the indexes the recommended query patterns require.

Data Flow Diagram (DFD)

How records move between your CRM, databases, queues, model providers, and users, including where PII is redacted, where retries happen, and where a human can intervene.

Fixed-price implementation roadmap

Sequenced phases with scope, acceptance criteria, effort, and a fixed price for each. The roadmap stands on its own, so you can execute it with InsightSol or with your own engineers.

Consultation intake

Tell us where the system is. We will come to the call prepared.

Four short steps. Your answers go to the engineer who runs the audit, and the last step opens scheduling.

  • 01Current architecture state and stack
  • 02Core bottleneck, in your words
  • 03Targeted stack, timeline, and budget scale
  • 04Contact details and audit kickoff scheduling

  1. 01

  2. 02

  3. 03

  4. 04

Step 1 of 4

Current architecture

Where the system is today

Which track fits your situation?

Audit FAQ

What CTOs ask before granting access.

Read the full FAQ

Most audits complete in two to three weeks from the day access is granted. Discovery takes the first few days, analysis and proof calls take the bulk of the time, and the written review and roadmap are delivered together with a live walkthrough.

Read-only access to repositories, logs, and a database replica is enough for the scaling track. We work under NDA, inside your access controls, and never need write access to production systems to complete an audit.

Then the audit has done its job. The written review explains exactly why, with the measured error rates or economics that led to the conclusion, and proposes the nearest version that would work. You keep every deliverable either way.

Yes. The roadmap, ERD, and DFD are written to be executed by any competent engineering team. Many clients run the first phase with us and later phases in-house, or use the roadmap to brief dedicated developers from InsightSol.

When you proceed with InsightSol on the roadmap, the audit fee is credited against the first implementation phase. Ask about this on the scheduling call and we will confirm it in writing.

Get the verdict before you commit the budget.

Book the audit kickoff. In two to three weeks you will know what to build, what it costs to run, and what it returns.

Book the Audit KickoffStart the intake form
  • Read-only access under NDA
  • Written review and diagrams
  • Fixed-price roadmap