DGTL AI
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, one timezone with your East Coast team, 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
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.
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
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
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
Implements agent orchestration and prompt workflows, handles monitoring and continuous evaluation.
variable
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 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.
Every module ships with senior practice leads, clear deliverables, and measurable outcomes. Engagements combine the modules you need into one scoped program.
Multi-step agents built on LangGraph, CrewAI, and custom orchestration for research, operations, and customer-facing workflows.
Retrieval-augmented generation over your documents with proper chunking, reranking, and evaluation so answers stay grounded.
Chatbots and voice agents for support, sales qualification, and internal ops, wired into your CRM, helpdesk, and knowledge base.
Fine-tuning open-weight models on Llama, Mistral, and Qwen families when off-the-shelf APIs fall short on accuracy or cost.
Golden datasets, offline evals, and production monitoring through tools like Braintrust and LangSmith to catch regressions before users do.
Systematic prompt design, versioning, and A/B testing, treated as code rather than a whiteboard exercise.
Policies, model risk registers, and human-in-the-loop controls aligned to NIST AI RMF and the EU AI Act risk tiers.
Training, deployment, and monitoring pipelines for classical ML and LLM workloads on SageMaker, Vertex, and self-hosted stacks.
n8n, Make, Zapier, and custom Python workers that remove repetitive internal work without creating brittle spreadsheet chains.
Embedding pipelines and vector stores in Pinecone, Weaviate, and pgvector for semantic search across content, products, and docs.
In-product generative features such as drafting, summarization, and image synthesis, shipped with guardrails and cost controls.
Personalization and recommendation engines for commerce, content, and B2B product suggestions, tied to your analytics events.
Demand, revenue, and churn forecasting using Prophet, XGBoost, and modern time-series models, delivered as dashboards or API.
Document parsing, OCR, defect detection, and image classification for manufacturing, healthtech, and commerce use cases.
Transcription, translation, sentiment, and call-intelligence pipelines using Whisper, Deepgram, and custom NLP classifiers.
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.
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.
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?”
We implement safety layers: input validation, output monitoring, fallback routing, and human-in-the-loop escalation. AI governance is built in, not bolted on.
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.
Metrics from recent DGTL AI engagements:
Query resolution
78% automated
AI agent handling portfolio queries for a fintech B2B platform 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
“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.”
Laura S.
VP of Operations, B2B commerce platform
The 15 tools we reach for most. We use plenty of others when the engagement calls for them.
Quito HQ, delivery across the Americas
One timezone with your East Coast team, year-round. Bilingual English and Spanish. No handoff at 5pm, no DST drift in March or November.
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.
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.
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.
Tell us what you want to automate. We’ll design the agent, build the guardrails, and ship it to production.