Data and AI engineering, shipped to production.
Audit-led, milestone-paced, no offshore handoffs. Same people from kickoff to ship backed by 14 years of engineering through DiscoverWebTech. Two practices, one accountable team.
Two practices.
One accountable team.
Data
Engineering
Practice / 01
Pipelines, warehouses, dashboards, and cost optimization for Series A–C SaaS teams. We rebuild the unsexy plumbing that drains your engineering hours.
| D1 | Data Audit |
| D2 | Pipeline Build |
| D3 | Cost Optimization |
| D4 | BI Implementation |
| D5 | Data Retainer |
| D6 | Conversion Tracking |
AI
Engineering
Practice / 02
Production AI systems for D2C, e-commerce, and SaaS. Chatbots, agents, copilots, recommendations plus the eval pipelines and security hardening that keep them running.
| A1 | AI Audit + Red-Team |
| A2 | Chatbot Build |
| A3 | AI Agent Build |
| A4 | AI Product Build |
| A5 | AI Retainer |
Most engagements start with the audit.
Two weeks. Written deliverable. Either becomes the scope for a build, or it's the only thing we do your call. The audit is the engagement.
Three phases.
No surprises.
Audit.
We assess your current stack, talk to engineering and business stakeholders, and produce a written scope document. You approve it before we write a line of code or run a single test. About 30% of audits surface that we're not the right fit we say so.
Build.
Sprint-based delivery. Friday demos. Shared Slack with your team. You see exactly what we're shipping each week and we flag risks before they become problems, not after. Production-grade from day one.
Operate.
Documentation, runbooks, training sessions. Either we keep running it as a retainer (D5/A5), or we hand off cleanly and your team owns it. Either path is fine the work is what matters, not who runs it long-term.
Outcomes shipped
to production.
Cut Snowflake spend 38% in six weeks.
Series B B2B SaaS, ~$15M ARR. Audit identified 4 oversized warehouses, 12 inefficient queries, and 3 dashboards refreshing every 5 minutes that nobody used. Annualized savings: $132K.
Replaced 3 FTEs of invoice processing with one AI agent.
Series A B2B fintech. Replaced 3 FTE-equivalent of manual invoice processing with an AI agent: extract → classify → validate → post to ERP. Eval pipeline on every change, human-in-loop for low-confidence (4% of volume).
23 critical issues fixed before launch not after.
Pre-launch red-team of a customer-facing AI assistant. Found prompt injection (3 vectors), PII leakage (1 critical), jailbreak susceptibility (medium), and rate limiting gaps. All shipped fixed, before launch.
Four claims.
Each one testable.
Practitioner-led, not consultant-led
Our founders write code and ship systems. No "strategy decks" disconnected from delivery. The people you talk to on day one are the people who ship.
Audit-first, then build
Every engagement starts with a 2-week audit. We earn the right to scope by understanding what's actually there. About 30% of audits end with us recommending against building.
Production-grade by default
Monitoring, eval pipelines, runbooks, and incident playbooks ship with every system. Nothing leaves prod undocumented. Your team can run it after we're gone.
Honest about what we don't do
If we're not the right fit, we'll say so and recommend alternatives. No engagement-stretching. No bait-and-switch with juniors.
What we don't do.
— We don't take on
- Real-time / sub-second streaming systems
- Pure ML research or academic AI
- Voice AI, robotics, computer vision
- Pre-revenue companies with no clear ICP
- Engagements under $5K
- "Make our deck look like AI" projects
— We'll happily refer you to
- Specialist real-time / Kafka / Flink shops
- ML research labs and academic partners
- Vision and voice AI specialists
- Earlier-stage advisory consultants
- Larger SI firms for $500K+ enterprise programs
- Generative design or marketing-creative agencies
Built on a 14-year bench.
Refocused for data + AI.
Siddharth Mishra
14 years building production systems. Leads AI product development, security, and architecture. 30+ AI agents shipped to production over the last 2 years.
mishrasiddharth.com →Shiwali Ratan Mishra
Leads data engineering practice and client strategy. Specializes in production-grade pipelines and the bridge between business teams and engineering.
shiwaliratanmishra.com →DiscoverWebTech
Our 14-year parent engineering firm. Still operating as the broader software-engineering practice; its senior engineers staff every Gigaflop project alongside the founders. USD-billed, fully on-record.
discoverwebtech.com →Things buyers
actually ask.
Q.01How do we get started?+
Q.02What does engagement pricing look like?+
Q.03Who actually does the work?+
Q.04Where are you based and how do you work with international teams?+
Q.05Can we just hire you for AI security / red-teaming?+
Q.06What if the audit shows we shouldn't build?+
Building data or AI?
Let's talk.
30 minutes. No slides. We'll talk about what you're building, where the risks are, and whether we're the right team. If not, we'll point you to someone who is.
Book a 15-min call → hello@gigafloptechlab.com