Available · Q2 2026 · 3 audit slots open Reply <4 hrs business hours ·  Book a 30-min call →
Co-founder · Gigaflop Techlab + DiscoverWebTech

The engineer you bring in when the work can't go wrong.

I build production data + AI systems for SaaS and D2C teams. 30+ AI agents in production. Audit-first, milestone-paced, no offshore handoffs. About 1 in 5 of my discovery calls end with me telling the client to hire in-house instead.

Book a 30-min call → ▶  Watch 2-min intro

— Siddharth Mishra, Indore · 14 years engineering through DiscoverWebTech · Working US · UK · EU · APAC

Siddharth Mishra
● Accepting Q2 work
— Intro

2-minute walkthrough: how I work, what I ship.

2:14 · 1080p · Indore · 2026
"What production AI actually requires."
Click to play · 02:14
— 00:00
Who I am, what I do
— 00:32
The two practices: Data + AI
— 01:14
How an engagement runs
— 01:48
When to hire instead
Years engineering depth, since 2012 (DWT)
AI agents shipped to production, last 24 months
Data pipelines shipped (Gigaflop + DWT)
1 in 5
Discovery calls ending with a "hire-instead" recommendation
Two practices · One accountable team

What I actually ship — data and AI.

Most companies need both. Stitching across two vendors is where projects die. I run both practices, with the same engineering bench and the same audit-first methodology. Pick one, or stack them in a single engagement.

— Practice 01 · Data Engineering From $4.5K

Data engineering

Pipelines, warehouses, dashboards, and cost optimization for Series A–C SaaS. The unsexy plumbing that drains your engineering hours.

  • D1 · Data Audit4–6 wks · $4.5K–$15K · written deliverable, scope doc
  • D2 · Pipeline Build8–16 wks · $15K–$40K · Snowflake / BQ / Databricks / Postgres
  • D3 · Cost Optimization6–8 wks · $12K–$30K · typically cuts spend 30–50%
  • D4 · BI Implementation10–14 wks · $20K–$50K · semantic layer + dashboards
  • D5 · Data Retainermonthly · $5K–$15K MRR · ongoing operate + build
200+ pipelines shipped · −38% avg cost cut
Discuss data work →
— Practice 02 · AI Engineering From $5K

AI engineering

Production AI for D2C, e-commerce, and SaaS. Chatbots, agents, copilots — with the eval pipelines and red-teaming that keep them running.

  • A1 · AI Audit + Red-Team4–6 wks · $5K–$15K · 47-vector adversarial test
  • A2 · Chatbot Build6–12 wks · $15K–$35K · RAG, evals, fallback, monitoring
  • A3 · AI Agent Build10–16 wks · $25K–$60K · multi-step + tool use + HITL
  • A4 · AI Product Build16–26 wks · $40K–$120K · end-to-end customer-facing
  • A5 · AI Retainermonthly · $2K–$10K MRR · eval, drift, on-call
30+ AI agents in prod · 23 critical caught (2025)
Discuss AI work →

Not sure which fits? Start with the audit.

2–6 weeks, fixed-price, written deliverable. ~70% convert to a build. The other 30% — I tell you not to build, or hire instead. Either is a win.

Book a 30-min call → hello@mishrasiddharth.com
Selected work · 2024–2026

Four cases I personally led.

Anonymized for confidentiality. Specific numbers. References available after a discovery call.

01
A1 → A3 · 11 weeks · Series A B2B Fintech · 2025

Replaced three FTEs of invoice processing with one agent.

Three-person ops team was eyeballing 12,000 PDFs a month. Hire #1 quit; hire #2 was burned out. The hard problem wasn't extraction (Claude vision was good enough) — it was validation, with fuzzy line-item matching against POs in NetSuite and explicit confidence scoring.

What I built: extraction pipeline → validation agent with tool use → confidence-based routing (>0.95 straight-through, 0.7–0.95 to a human review queue with prefilled answer, <0.7 human-from-scratch). 1,200-case eval suite on every prompt change. Drift monitoring.

96.3% straight-through ~$180K/yr saved 95.8% @ month 9 Claude Sonnet · NetSuite · Postgres
96.3% straight-through
02
A1 audit · 4 weeks · Series B B2B SaaS · 2025

Twenty-three critical issues, caught before launch.

The team had run an internal review and felt good about it. I came in for an external red-team across 47 adversarial vectors over four weeks.

Findings: prompt injection (three vectors), PII exfiltration (one critical — cross-tenant via crafted query), tool-call abuse (refund tool callable on adversarial input), jailbreak susceptibility (four paths), rate-limit gaps. All shipped fixed before launch.

23 critical caught $8K audit fee 0 post-launch incidents (12mo) 47-vector adversarial test
By severity
03
A4 product build · 22 weeks · D2C Beauty · 2024–25

An AI shopping assistant that actually deflects tickets.

A D2C beauty brand at $30M revenue. Their vendor-SaaS chatbot was deflecting nothing — customers bounced through it on the way to a human. I rebuilt as a full A4 product: RAG over product catalog + style preferences + cart context, polished customer-facing UX, multi-tenant backend for four sub-brands.

Stack: Claude Sonnet for reasoning, GPT-4o-mini for cheap reformulation, Pinecone for retrieval, custom React frontend.

73% deflection 4.1/5 CSAT (vs 3.8 baseline) $0.04 per resolved ticket 4 sub-brands · multi-tenant
73%
Tickets deflected
04
D3 → D5 · 8 weeks · Series B SaaS · 2025

Cut Snowflake spend 38% in six weeks.

Series B B2B SaaS, ~$15M ARR. Their Snowflake bill had doubled in three quarters with no corresponding volume change. The CFO had stopped approving new BI tool requests.

The audit found 4 oversized warehouses, 12 inefficient queries, 3 dashboards refreshing every 5 minutes that nobody used, and a dbt model running incremental-broken. Fixed all of it in six weeks. Set up cost monitoring + Slack alerts so it doesn't drift back.

−38% Snowflake spend $132K/yr annualized savings 5.5× ROI year one Holding 9 months later
audit
Spend, before → after
From the work

What clients actually said.

"This is the cheapest insurance we've ever bought. We had run an internal review and felt OK. Siddharth's red-team caught twenty-three things we'd have shipped — including one cross-tenant PII path that would have ended us."

VP
VP EngineeringSeries B B2B SaaS · A1 red-team

"We didn't want a science project. We wanted a system that worked while we slept. That's what we got. Nine months in, the agent's still at 95.8% straight-through — and our ops team is down from three to one."

HO
Head of OperationsSeries A B2B Fintech · A3 agent build

"He told us, in the second discovery call, that we didn't need to build the thing. Walked us through a 4-hour fix instead and didn't charge for the diagnostic time after the audit. I sent him three referrals that quarter."

FD
FounderSeries A SaaS · "hire-instead" outcome
How an engagement runs

Three phases. No surprises.

Same rhythm whether it's a Snowflake pipeline or an AI agent build. Scope it, ship it, hand it off clean.

01
Weeks 1–2 · Audit

Diagnose, then scope.

I assess your current stack, talk to engineering and business stakeholders, and produce a written scope document. You approve it before I write a line of code. About 30% of audits surface that I'm not the right fit — and I say so.

02
Weeks 3–10 · Build

Sprint-based, Friday demos.

Shared Slack with your team. You see exactly what's shipping each week — and I flag risks before they become problems. Production-grade from day one. No prototypes left in prod.

03
Final week + ongoing

Handoff, or operate.

Documentation, runbooks, training. Either I keep operating it as a retainer (D5/A5), or your team owns it. Either is fine — the work is what matters, not who runs it long-term.

Now · this week

The current state.

BuildingAn A4-tier AI product for a Series C SaaS — eval pipeline + HITL UI + drift monitoring. Ships July.
Red-teamingPre-launch audit. 11 critical, 4 high found so far. Three weeks in.
WritingLong-form on why most "AI agents" in 2026 are elaborate prompts.
AvailableQ2 audit slots — 3 open. Discovery calls via cal.com/gigafloptechlab.
Writing

Essays & notes.

Read something useful? Or have something to ship? Either way — book a call.
FAQ · before the call

Things buyers actually ask.

How fast can we start?

+

Discovery call within the same week. Audit kickoff within 1–2 weeks of contract sign. Build kickoff immediately after audit sign-off. Most clients move from "first email" to "audit kicked off" inside 10 business days.

What does pricing actually look like?

+

All fixed-scope, fixed-price. No hourly billing. Audits run $4.5K–$15K. Builds run $12K–$120K depending on tier. Retainers run $2K–$15K MRR.

Specific quote comes in the proposal, after the audit. I don't price builds off discovery calls — I won't know what to ship until I've seen what's there.

Who actually does the work?

+

Me, plus a senior engineering bench from DiscoverWebTech (the 14-year parent firm). No bait-and-switch with juniors. The architect you meet on the discovery call is in the working sessions every week. I don't subcontract to anonymous offshore teams.

Will you sign NDA, MSA, DPA?

+

Yes to all three. Templates reviewed by client counsel; also fine working off your paper. SOC 2 Type 2 in process (target Q3 2026). Ask for the security overview to brief your security team early.

What if the audit says we shouldn't build?

+

I tell you. About 30% of audits end with "your existing system is fine, here's a smaller fix" or "you don't need this — here's what to do instead." The audit fee is the engagement; you don't owe me a build. I'd rather lose a large engagement than push a project that shouldn't ship.

Can I talk to a reference?

+

Yes — after the discovery call, brokered with the client's consent. Live references from each tier (audit, agent build, full product, cost optimization). Some clients are also happy to do a 30-min call directly.

— Talk to me

30 minutes. No slides.

If you're building data or AI infrastructure and want a real conversation — book a discovery call. A founder runs every call. If I'm not the right team, I'll point you to someone who is.

Book a 30-min call → hello@mishrasiddharth.com
P.S. Audits start at $4.5K. About 70% convert to a build. The other 30% — I say so. Either way, the audit is the cheapest way to find out what's actually broken.
Book a call
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