        
        Consulting × Building × Applied AI
      

AI that ships, not slideware.

We are a senior team of engineers and consultants. We work out what is worth building, then build it and hand over the working software — no research phase that ends in a deck.

Start a project → See the work

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        LLM applications◆Agentic workflows◆Process automation◆RAG & data plumbing◆Evals & guardrails◆AI strategy◆Team enablement◆
        LLM applications◆Agentic workflows◆Process automation◆RAG & data plumbing◆Evals & guardrails◆AI strategy◆Team enablement◆
      

Years of experience

Custom AI agents built

Team members,
different backgrounds

Websites built

From Consulting to Building

Capabilities

01

AI agents→

Agents that act inside your systems — research, triage, drafting — with permissions, approval gates and audit trails.

tool useguardrailsevals

02

Automations→

The unglamorous wins: intake, routing, reconciliation and reporting, wired into the tools your team already uses.

workflow designintegrationsdocument AI

03

Applications→

Internal copilots and review tools built around one workflow, shipped as software your team actually opens daily.

LLM appsRAGUX

04

Websites→

Marketing and product sites that load fast, read clearly and are simple for your team to keep updated.

designbuildCMS

05

Web apps→

Dashboards, portals and internal tools — auth, roles and real data, built to survive real usage.

frontendAPIsdata

06

Custom MCP servers→

We wrap your internal systems in MCP servers so any AI client can use them safely, with scoped access.

MCPAPIsaccess control

How We Work

STEP 01 45 minutes

Discovery call

You walk us through the process that hurts. We tell you on the call whether AI is the right tool — and if it isn’t, we say so.

STEP 02 1 week

Blueprint week

One week with your team to settle what to build and how: workflows, data, integrations, success metrics and a cost ceiling. You end with a spec, not a deck.

STEP 03 4–8 weeks

We build the tool

Four to eight weeks to working software in your environment — then the repo, docs, runbooks and a trained team, with us on call for 30 days.

Financial services

Healthcare & life sciences

Manufacturing

Logistics & supply chain

Retail

E-commerce

Real estate

Industries We Serve

Case Studies

Why Choose Us

Dimension

Large consultancy

Us

Who does the work

✕Partners sell, a rotating bench of juniors delivers

✓The people who pitched, writing the code

What you receive

✕Assessment deck, roadmap, a pilot proposal

✓Running software in your cloud, plus the repo

Time to first value

✕Two quarters of discovery before a line of code

✓A usable build in week three, every time

Commercials

✕Blended day rates, change requests, overhead

✓Fixed scope, fixed price — roughly 1/5th of a consulting firm’s bill

After go-live

✕Managed-service retainer you can't exit

✓Your code, your keys, your team trained on it

Access

✕Escalation paths and steering committees

✓A shared channel with the builders, same day

Tell us what's
broken.

Email

growth@diagonally.in

Based in

Bengaluru, Karnataka, India

Case 02 · Fashion retail

A functional marketing brain

Problem

India’s leading fashion brand spends INR 30–50 lakh a month on Google Search, with three decisions recurring every week: search-term negation, device bids, location bids.
The team already pastes Google Ads CSVs into a frontier model — around 90% accurate, but structurally limited.
It optimises on Google-reported revenue, not the Adobe revenue the business actually trusts.
On thin data it confidently recommends negating healthy terms and cutting bids on noise.
Nothing is logged — no record of what changed, on what evidence, or whether it worked, so knowledge decays.
The Google data estate is fully manual; only three live programmatic feeds exist.

Solution

A weekly scheduled pipeline for Google Search — one channel, three tasks — in five components.
Ingest: Google Ads and Adobe exports at four grains — campaign, search term, device, geo.
Join: a campaign-name parser derives structure from existing naming, with Adobe revenue attached.
Gate: deterministic checks for data sufficiency, brand-term protection, negation history and test exclusion. Fails are withheld, never recommended.
Reason: one frontier-model call over the gated table, evidence attached.
Log: recommendation → accept/reject → outcome at the next run.
Shadow mode only: no write access, one reviewer, around 30 minutes a week.

Business Impact

Scope narrowed from 4 phases across all channels to 1 channel and 3 tasks; architecture cut from 10 layers to 5 components, on a 9-week plan with 4 milestones.
Running cost fixed at 0.16–0.67% of annual Search spend.
Diagnostics in the live account: a pure brand term bid at 5–13x its siblings — roughly INR 2.55 lakh a year at run rate.
The largest budget line flagged “95% products excluded”, with poor ad strength on another major campaign.
Terms with 130–210 clicks shown to have a 25–41% chance of zero conversions by chance alone — the exact case the gate blocks.
Contracted acceptance: ≥90% agreement with the team’s own past negation decisions, 100% brand-term protection, and sample size plus confidence on every recommendation.
ROAS within 2% of Adobe, weekly run under 30 minutes, and 3 clean consecutive shadow runs.

Snapshots of the Tool

Action queue — eight recommendations the Brain caught

Landing-page forensic — funnel decomposition

Monday brief — cross-channel substitution

Case 01 · Pipes manufacturing

Inventory intelligence across 9 plants

Problem

Manufacturers know how much stock they have, but not how long it will last or where it is stuck.
One plant sits overstocked while another runs dry on the same material, and nobody sees both at once.
Reordering runs on fixed thresholds, so shortages surface only after production has already stopped.
Meanwhile a large share of working capital — 27% in this dataset — sits locked in slow-moving or at-risk stock, invisible until month-end.

Solution

A single control tower for multi-site raw material operations, covering 234 materials across 9 plants and depots.
It grades every material by days of cover against its own lead time and projects the stockout date from real consumption trends.
It matches surplus at one site to a predicted shortfall at another, then recommends the transfer route and mode — road, rail, sea or air — costed for weather, road condition and urgency.
Around this sit supplier scorecards, PO lifecycle tracking, order-to-BOM material checks and 12 automations that raise, escalate and rebalance on their own.

Business Impact

60–80% fewer stockouts and 10–15 points better on-time delivery to customers.
15–25% of inventory value released — ₹60L–₹100L on this dataset — by rebalancing instead of buying.
10–20% lower freight per transfer, plus ₹5L–₹15L a year saved by avoiding emergency air shipments.
8–12 hours a week saved per buyer, with transfer decisions dropping from two days to under ten minutes.

Snapshots of the Tool

Historical stock trend and stockout forecast