DGTL AI
AI Agents That Do Real Work
Production AI agents, RAG platforms, and governance frameworks built alongside Secure and Data so your deployment survives procurement review instead of stalling inside it. Headquartered in Quito, inside your East Coast team’s working day all year, delivery across the Americas.
You've seen the demos. Your team has built a proof of concept. Maybe you even shipped a chatbot that answers basic questions about your product.
But that's not what your business needs. You need AI that actually does work, that researches prospects before a sales call, triages support tickets with full account context, or runs multi-step onboarding workflows without human intervention.
The gap between an AI demo and a production-grade agent is enormous. Most teams spend months choosing between models and frameworks, build something that works in a notebook, and then discover it breaks when real users send unexpected inputs. Governance, monitoring, and escalation paths get skipped. The result is an AI project that impresses in a board meeting but never makes it to production.
Better together
Hiring DGTL for one practice works. Here is why most engagements use more than one.
AI works closely with Build (embedding agents into your product), Secure (AI governance, bias audits, and regulatory compliance), Data (connecting agents to your data infrastructure), Marketing (AI-powered content and personalization), and Sales (outreach automation and prospect intelligence). Our fintech client's AI agent handles 78% of portfolio queries without human intervention, that required Build, AI, and Secure working together from day one.
How we staff AI
Every engagement lands the seniority your scope needs. No bait-and-switch between the sales call and the kickoff. No junior-heavy delivery teams behind a Principal on the deck.
10+ years
Principal AI Engineer
Sets model architecture and evaluation strategy, leads the MLOps design, negotiates governance requirements with enterprise buyers. Has deployed production AI in a regulated industry.
5 to 8 years
Senior AI Engineer
Builds RAG pipelines, fine-tunes open-source models, owns the evaluation harness. Fluent in both the ML side and the web stack that hosts it.
3 to 5 years
Mid AI Engineer
Implements agent orchestration and prompt workflows, handles monitoring and continuous evaluation.
variable
AI Governance Specialist
EU AI Act and NIST AI RMF compliance, model risk documentation, bias testing, audit trail design. Brought in for any deployment that will face a regulator.
DGTL Readiness Index
The AI dimension, before and after.
The AI dimension measures data readiness, model governance, production deployment, and team literacy. We move the number by getting your first production agent out the door with the same compliance posture as the rest of your platform.
Day 1, typical pre-engagement
2.0 / 5
Day 90, after cross-practice work
3.5 / 5
Median delta across 90-day engagements: +1.5 points. Composite profile from discovery engagements with mid-market companies. See the full 8-dimension framework.
What we do
Every module ships with senior practice leads, clear deliverables, and measurable outcomes. Engagements combine the modules you need into one scoped program.
AI Agents
Multi-step agents built on LangGraph, CrewAI, and custom orchestration for research, operations, and customer-facing workflows.
RAG Systems
Retrieval-augmented generation over your documents with proper chunking, reranking, and evaluation so answers stay grounded.
Conversational AI
Chatbots and voice agents for support, sales qualification, and internal ops, wired into your CRM, helpdesk, and knowledge base.
Model Fine-tuning
Fine-tuning open-weight models on Llama, Mistral, and Qwen families when off-the-shelf APIs fall short on accuracy or cost.
LLM Evaluation
Golden datasets, offline evals, and production monitoring through tools like Braintrust and LangSmith to catch regressions before users do.
Prompt Engineering
Systematic prompt design, versioning, and A/B testing, treated as code rather than a whiteboard exercise.
AI Governance
Policies, model risk registers, and human-in-the-loop controls aligned to NIST AI RMF and the EU AI Act risk tiers.
MLOps
Training, deployment, and monitoring pipelines for classical ML and LLM workloads on SageMaker, Vertex, and self-hosted stacks.
Process Automation
n8n, Make, Zapier, and custom Python workers that remove repetitive internal work without creating brittle spreadsheet chains.
Vector Search
Embedding pipelines and vector stores in Pinecone, Weaviate, and pgvector for semantic search across content, products, and docs.
Generative Features
In-product generative features such as drafting, summarization, and image synthesis, shipped with guardrails and cost controls.
Recommendations
Personalization and recommendation engines for commerce, content, and B2B product suggestions, tied to your analytics events.
Forecasting
Demand, revenue, and churn forecasting using Prophet, XGBoost, and modern time-series models, delivered as dashboards or API.
Computer Vision
Document parsing, OCR, defect detection, and image classification for manufacturing, healthtech, and commerce use cases.
Speech and NLP
Transcription, translation, sentiment, and call-intelligence pipelines using Whisper, Deepgram, and custom NLP classifiers.
How it works
AI Discovery
We map your workflows, identify automation opportunities, and prioritize by ROI. The output is a ranked list of agent candidates with effort estimates and expected impact.
Architecture & Data
We design the agent architecture, set up the data pipeline, and configure integrations. If your data isn’t ready (it usually isn’t), we fix that first.
Agent Development
We build in 2-week sprints with continuous evaluation. Every agent goes through structured testing, not just “does it work?” but “does it work when users do unexpected things?”
Guardrails & Governance
We implement safety layers: input validation, output monitoring, fallback routing, and human-in-the-loop escalation. AI governance is built in, not bolted on.
Production & Monitoring
We deploy to production with real-time monitoring, quality dashboards, and alerting. You see exactly how your agents are performing and where they need improvement.
Engagement models
Three ways to buy AI engagements. The difference is what happens at the end.
Same team underneath all three. One MSA, one SOW per engagement. What changes is how long you commit, what flexes once work starts, and who sits on the team. We scope every engagement to what you’re building instead of dropping you into a published tier.
01Project | 02Retainer | 03Embedded Team | |
|---|---|---|---|
| At the end | ProjectIt ends. | RetainerIt keeps going. | Embedded TeamIt renews. |
| Ideal stage | ProjectPre-launch, MVP, redesign, audit, certification | RetainerGrowth phase, ongoing optimization, scaling | Embedded TeamScale phase, fractional executive leadership |
| Commitment | Project4–16 weeks | RetainerMonth-to-month after a 3-month minimum | Embedded TeamQuarterly, renewable |
| How it starts | ProjectPaid discovery sprint (1–2 weeks) | RetainerKickoff + priorities alignment session | Embedded TeamTeam matching + onboarding week |
| Who’s on it | ProjectCross-practice squad assembled for your project | RetainerDedicated team lead + rotating specialists | Embedded TeamFull-time embedded specialists |
| What flexes | ProjectDefined deliverables and timeline | RetainerFlexible within allocated hours | Embedded TeamFlexible within team capacity |
| Where we’d start | We’d start with a Project: one working agent or pipeline in production beats a roadmap. | ||
Not sure which one fits? Tell us what you’re working on and we’ll recommend the right model based on your goals, timeline, and budget.
We custom-scope every engagement. Most focused engagements start in the five figures, and multi-practice transformations scale into the six and seven figures. Retainers and embedded teams are priced monthly, based on seniority and hours.
See the full comparisonResults
Metrics from recent DGTL AI engagements:
Query resolution
78% automated
AI agent handling portfolio queries for a fintech B2B platform
Support load reduction
50%+
Customer-facing agent deflecting L1 support tickets
Outbound personalization
3× efficiency
AI-powered prospect research and copy generation
Time to production
4–8 weeks
Typical agent deployment from discovery to live
What clients say
“DGTL replaced our chatbot with an agent that actually resolves tickets, pulls account data, checks order status, handles refunds. Our support team went from drowning to focused on the stuff that actually needs a person.”
VP of Operations, B2B commerce platform
Representative example. Roles and industries are shown; specific companies and individuals are not identified until signed releases are available.
Our stack
The 15 tools we reach for most. We use plenty of others when the engagement calls for them.
Quito HQ, delivery across the Americas
UTC-5 all year, an hour or less from your East Coast team in either season. Bilingual English and Spanish. No handoff at 5pm, and nothing waits overnight when the US clocks change in March and November.
Why this matters
The problem
Your team spent three months building an AI prototype that works in demos but breaks in production. Users send unexpected inputs, the model hallucinates, and your engineers are spending more time fixing the AI than building your core product. And you don't have the governance, monitoring, or escalation paths to deploy it safely.
What we do about it
We design, build, and deploy production-grade AI agents with proper guardrails, evaluation frameworks, and monitoring, so your AI works reliably at scale, not just in a notebook.
What you get
You ship AI features that actually reduce costs and improve operations, with the confidence that they won’t embarrass your brand or break your workflows.
Frequently asked questions
Ready to deploy AI that works?
Tell us what you want to automate. We’ll design the agent, build the guardrails, and ship it to production.