10+ years shipping production systems◆ ML · NLP · LLMs · Data Pipelines · MLOps◆ Integrated into your existing stack◆ You own the code. Fully.◆ 6 weeks to first production AI feature◆ 10+ years shipping production systems◆ ML · NLP · LLMs · Data Pipelines · MLOps◆ Integrated into your existing stack◆ You own the code. Fully.◆ 6 weeks to first production AI feature◆
AI Engineering Partner · Remote-first

Your competitors
are already
shipping AI.

Dwayo partners with engineering teams at scale-ups to ship production AI in weeks — integrated into your existing stack, owned by your team, with zero proprietary lock-in. We close the gap before it becomes permanent.

Book a free 30-min call → See what we've fixed ↓
6wk
To first production AI feature
7mo
Saved vs. hiring in-house
0
Proprietary lock-in. Ever.
Stack
LangChain LlamaIndex OpenAI Anthropic Claude Hugging Face PyTorch FastAPI Kubernetes Terraform AWS / GCP / Azure Pinecone Weaviate RAG Pipelines Vector DBs MLflow Apache Spark Hadoop NLTK / spaCy IoT Pipelines LangChain LlamaIndex OpenAI Anthropic Claude Hugging Face PyTorch FastAPI Kubernetes Terraform AWS / GCP / Azure Pinecone Weaviate RAG Pipelines Vector DBs MLflow Apache Spark Hadoop NLTK / spaCy IoT Pipelines
What we do

Three disciplines.
One unified team.

01
◈
AI Consulting & Strategy

We audit your business, identify high-leverage AI opportunities, and build a phased roadmap grounded in ROI — not hype. No vague frameworks, just execution-ready plans.

AI Audits Roadmapping Use-case scoping Build vs. buy
02
⬡
End-to-End AI Product Dev

From prototype to production. We design and engineer full AI products — LLM applications, agents, RAG systems, and custom model fine-tuning — built to survive real-world scale.

LLM Apps Agents & Automation RAG Systems Fine-tuning
Guardrails & evals built-in
03
⬟
AI Infrastructure & MLOps

Reliability and scale are non-negotiable. We build the pipelines, monitoring, CI/CD, and infra that keeps your AI system performant, observable, and cost-efficient in production.

ML Pipelines Model Serving Monitoring Cost Optimization
Privacy-first architecture
Hard-won experience

What we've seen break.

Not case studies. Specific failure modes we've diagnosed and fixed — the kind that don't show up until production.

01
RAG pipeline returning confident wrong answers

Root cause: no retrieval eval layer. The vector search was returning plausible but semantically off chunks. The LLM hallucinated confidently on top of bad context. Nobody knew until a user screenshot went viral internally.

Fix → Retrieval precision/recall benchmarks + chunk-level confidence scoring before any answer is surfaced.
02
LLM agent taking unintended actions in production

Root cause: no output schema enforcement on tool calls. The agent was given a list of actions it could take. With ambiguous prompts, it chose the wrong one — at scale, automatically.

Fix → Strict output schemas + action whitelists + human-in-the-loop gates on all write operations.
03
Inference costs 40× higher than projected at scale

Root cause: nobody modelled token usage at volume during the design phase. A feature that cost $0.002 per call looked fine in staging. At 500k calls/day it was a $30k/month surprise.

Fix → Token budget modelling in the architecture phase + cost dashboards from day one of production.
04
Model provider API change breaks entire product

Root cause: direct coupling to one provider's API throughout the codebase. When they deprecated a parameter, every call broke simultaneously. The fix took three weeks.

Fix → Provider abstraction layer from day one. Swap models in config, not in 200 files.
How we work

Structured for speed.
Built for depth.

01
Stack Audit & Architecture Review

We map your existing infrastructure — databases, APIs, data pipelines, auth systems — and identify exactly where AI slots in without breaking what works. You give us access. We give you a gap analysis.

↳ Deliverable: Stack map + integration risk doc
Week 1
02
Technical Blueprint & Trust Spec

A precise architecture doc: system diagrams, model selection rationale, data flow maps, cost projections, and a written Trust Spec covering privacy, guardrails, and compliance — before a single line of code.

↳ Deliverable: Architecture doc + Trust Spec PDF
Week 1–2
03
Prototype → Your Engineers Review It

A working proof of concept against your real data. Your engineers review the code, ask questions, push back. We iterate. No black boxes — your team can read everything we write.

↳ Deliverable: Reviewed, commented prototype + eval baseline
Week 2–4
04
Production Build, Hardening & Evals

Full system built with logging, eval suites, guardrails, fallback logic, and CI/CD. Every milestone has a benchmark. Nothing ships without passing a defined quality threshold.

↳ Deliverable: Production system + eval dashboard
Week 4–10
05
Handoff: You Own Everything

Full source code, infra configs, runbooks, and a live knowledge transfer session with your team. IP transferred in writing. You can operate this without us from day one of handoff.

↳ Deliverable: Full codebase + runbook + IP transfer agreement
Week 10–12
Client outcomes

What we've actually delivered.

Clients are kept anonymous by agreement — but outcomes are real, documented, and verifiable on a call.

SaaS · B2B · Series A
Reduced support ticket volume by 64% after deploying an LLM-powered triage and auto-resolution layer on top of their existing Zendesk + Postgres stack. No data migration. Live in 5 weeks.
RAG Pipeline Zendesk Integration 5 weeks
FinTech · Lending · Scale-up
Built a document intelligence system that extracted structured data from unstructured loan applications with 94% accuracy — replacing a 12-person manual review team for the first-pass triage step.
Document AI Structured Extraction 8 weeks
E-commerce · Marketplace · $40M ARR
Shipped a personalised AI recommendation engine integrated directly into their existing Django backend. Avg order value up 22% within 60 days of launch. Inference cost held under $800/month at 2M daily requests.
Recommendation Engine Django Integration 10 weeks
⚠

Specific company names, team contacts, and supporting metrics are shared under NDA on discovery calls. We don't publish client names without written permission — but we don't expect you to take outcomes on faith either.

Who you're working with

Not a consultancy.
An engineering team.

Devji Chhanga
Devji Chhanga
Co-Founder & AI Engineering Lead, Dwayo
10+ years building scalable systems across cloud infrastructure, ML/AI, Big Data, and NLP. Shipped production-grade solutions for startups and enterprise clients — from distributed data pipelines on Apache Spark to LLM-powered products. Founded Dwayo because most companies have great engineers who haven't shipped AI in production yet — and that gap is closeable with the right team alongside them.
10+ years engineering ML · NLP · Big Data · LLMs Cloud-native systems
LinkedIn → linkedin.com/in/idevji
01
We tell you when we're not the right fit. If your problem doesn't need AI, we'll say so. We've turned down projects that weren't ready — and those clients came back when they were.
02
We write code your team can read. Every PR is documented. Every architecture decision is explained. We build systems that your engineers can own — not ones that require our continued involvement.
03
We price by milestone, not by hour. You know what you're getting before you sign. No scope creep invoices. No "that's out of scope" surprises mid-project.
Trust & Safety

AI that your legal team
won't flag.

We know the real blocker isn't budget — it's fear. Fear of a hallucination embarrassing your company, a data leak, or a system you can't audit or control. We treat these as engineering problems with engineering solutions.

🔒
Privacy
Data Privacy & Confidentiality

Your data never trains a third-party model. We architect data flows with strict isolation — what goes in stays in your environment.

  • No data leakage to public model providers by default
  • On-premise or VPC deployment options for sensitive workloads
  • PII redaction pipelines before any LLM interaction
  • Audit logs on every inference call, queryable at any time
🛡
Reliability
Hallucination & Output Control

Generative AI is probabilistic — but unreliable outputs are an engineering failure, not an inevitability. We build eval frameworks that catch bad outputs before users do.

  • Automated eval suites run on every deployment
  • Confidence scoring & fallback logic for uncertain outputs
  • Human-in-the-loop checkpoints for high-stakes decisions
  • Output schema enforcement — structured, validated responses
⚖️
Compliance
Regulatory & Compliance Risk

GDPR, SOC 2, HIPAA, the EU AI Act — regulatory surface area is growing fast. We design systems with compliance baked in, not bolted on after the fact.

  • Data residency controls — processing stays in your required region
  • Model cards & documentation for regulatory audit trails
  • Bias & fairness testing as part of every eval pipeline
  • Right-to-explanation architecture for regulated decisions
🔓
Control
Vendor Lock-in & Loss of Control

We don't build systems that only work if you keep paying us — or if OpenAI doesn't change their API. Portability and ownership are design constraints, not afterthoughts.

  • Model-agnostic architecture — swap providers without rebuilding
  • Full source code & IP ownership transferred at handoff
  • Comprehensive runbooks so your team can operate independently
  • No proprietary black-box wrappers — open, auditable stack

Every Dwayo engagement includes a Trust & Safety Spec — a written document detailing exactly how your system handles privacy, failures, and compliance before a line of code is written.

Full Trust Framework →
The honest question

Why not just hire in-house?

It's the right question. Here's the honest answer.

Dwayo
In-house ML hire
Big consultancy
Time to first production feature
6 weeks
6–9 monthshiring + onboarding + ramp
4–6 monthsdiscovery & scoping alone
Annual cost
Project-basedno benefits, no equity, no HR overhead
$280–420Ksalary + benefits + equity + recruiting
$500K–2M+billed by the hour, forever
Who owns the IP
You. Fully.transferred in writing at handoff
You
Negotiatedoften encumbered with their frameworks
Coverage
Strategy + Product + MLOps
One specialityyou still need a team around them
Variesdepends on which practice you're in
Risk if it doesn't work out
Lowmilestone-based, defined exit points
Highseverance, rehiring, 6-month gap
Highcontract penalties, knowledge locked in their heads
Can operate without you after
Yes — by design
Yes
Rarelydependency is their business model

In-house is the right answer eventually — once you know exactly what you're building and need someone to own it long-term. We get you there faster, at lower risk, and hand over everything when you're ready.

Get started

Tell us what
you're trying to build.

We'll tell you in 48 hours whether we can help — and if we can't, we'll tell you that too. No pitch deck. No retainer proposal. Just a straight answer.

Book a free 30-min call → Download Dwayo AI ROI framework ↓

Or email us directly: [email protected]