Solve your software
problems, with far
fewer people
in between.

Tekspaz builds AI systems for the parts of software that cost the most and move the slowest: deciding what to build, building it, and paying to run it.

Book a walkthrough See the three products

Intent

A business problem, written the way a business writes it.

Plan

Requirements, architecture, data model, APIs, tasks, estimates, risks.

Build & test

Agents write the code, run the tests, fix what breaks, review the diff.

Cost optimization

Once it runs, it costs. The third product works on the data platform underneath — Snowflake and Databricks — and takes 30–60% out of credit spend.

30–60%

Credit spend removed

Products in the chain

Chennai

Engineering base

SnowflakeDatabricksSolution designAgentic code generationData engineeringCost governanceCloud migrationEnterprise integrationTest automationPlatform observabilitySnowflakeDatabricksSolution designAgentic code generationData engineeringCost governanceCloud migrationEnterprise integrationTest automationPlatform observability

Platform

Everything between the
problem and production.

Three products. Two of them run in sequence; the third works on what the first two leave running.

One plan, not a deck

Discovery output that a coding agent can consume directly — requirements through estimates, with the reasoning attached.

Agents that run the loop

Generate, test, diagnose, fix, review. Nothing reaches the application on the strength of a first draft.

Your account, your data

The cost platform installs natively where your warehouse already lives. No credentials handed over, no metadata leaving the tenancy.

Evidence with every change

Recommendations carry the query and the hour that produced them, so your team approves a change rather than accepting a verdict.

The software factory

Two products,
run in order.

The first decides what to build; the second builds it.

Stage one — planning

Godsy

What exactly should we build, and how should it be designed?

Godsy turns a problem statement into a build-ready plan. A panel of AI specialists interrogates the requirement, researches the options, designs the solution and argues with itself until the plan holds up — the same conversation a good discovery team has, without the six weeks.

What comes out is a Plan Bundle. It is written to be handed straight to a coding agent or a development team, not filed as a deck.

What the Plan Bundle containsProduct Manager shapes 2 of these.

RequirementsWorkflowsArchitectureData modelAPIsUIDevelopment tasksEstimatesRisksValidation

Stage two — execution

Coder AI

How do we build it?

Coder AI takes a structured plan and returns working software. It orchestrates coding agents across the lifecycle — splitting requirements into tasks, generating code, running tests, diagnosing what failed, fixing it, reviewing the change and assembling the application as it goes.

Paired with Godsy it closes the loop: a business problem moves from idea to plan to code to a tested, working application, with a small team steering instead of a large one typing.

Input: A Godsy Plan Bundle, or an existing structured specification
Output: Tested, reviewed code, assembled into a running application
Your job: Direction and judgement — approve, redirect, ship

Platform economics

What it costs you every month once it is live.

A different problem from building software, and a different product.

Runs inside your account

Cost Optimization

Why is the data platform bill this large, and what can be changed safely?

Most Snowflake and Databricks spend is not lost to bad SQL. It is lost to compute that is awake when nobody is asking it anything, sized for a workload that no longer exists, and scanning far more data than the answer needs.

The platform installs as a native application inside your own account. Query history, warehouse telemetry and table statistics are read where they already live — no credentials handed over, no metadata leaving your tenancy.

Three levers, applied in that order, take 30–60% out of credit spend.

L1Idle time and scheduleWarehouses and clusters suspend on observed usage rather than a default timeout, and batch work moves to windows where it is not competing with interactive users.
L2Right-sizingSize, cluster count and scaling policy are matched to the workload actually running on them rather than the one they were provisioned for a year ago.
L3Scan and storage efficiencyClustering, partition pruning and table layout cut how much data each unchanged query has to read.

Where the spend goesIllustrative, at the midpoint

Each lever removes a different kind of waste, so the savings add rather than overlap.

SQL engine

Deterministic. Reads query history, warehouse telemetry and access patterns to find the waste already visible in the account.

ML engine

Predictive. Learns the workload's daily and weekly rhythm and proposes changes ahead of demand.

No analyst query is rewritten and no dashboard changes.
Recommendations run in observe-mode first.
Changes are applied by your platform team, or by the app under a policy your team sets.
Savings are measured in credits against your own baseline.

How engagements start

One problem, measured, before anything scales.

We are an engineering company, not a self-serve product. Engagements are scoped and priced per problem.

Scope one problem

A single requirement or a single account. Not a transformation programme — one thing with a measurable answer.

Run the assessment

Godsy produces a Plan Bundle, or the cost platform measures your baseline in credits. You see real output before committing.

Pilot with your team

We build alongside your engineers rather than beside them. Your team approves every change that lands.

Scale what worked

Extend to the next workload once the first one has numbers attached to it.

Delivered for

NEC
Reliance
Casagrand
Bentley
Street Matric

Start with one problem.

Bring a real requirement or a recent platform bill. We will run it through the relevant product and show you what comes out — the plan, the code, or a measured savings estimate — before you commit to anything.

Email the team Call +91 98841 88841