Data hyperautomation services and enterprise AI enablement for teams stuck in pilot purgatory. We build the AI-ready data foundation, provide MLOps consulting services & LLMOps pipeline engineering, scale RAG, put AI agents in operations, backed by responsible AI framework deployment. Get production-grade from day one. Your data. Your models. Your evals.
Tech Experts
Global Clients
You're a CTO or VP of engineering staring at five years of legacy debt. A cloud migration that's stalled. A CEO asking why shipping is so slow. If that's your day, application modernization is the solution. We start with an AI readiness assessment, one that shows where the platform is blocking you. You need it fixed - not rebuilt, not risked. That's what this page is for.
SCENARIO 01
5 years of architectural debt is blocking your AI initiatives. Failed to run retrieval pipelines, vector workloads, or agentic process automation. You need modernization that ships parallel with AI work, not a rebuild that has to finish first.
"We unlocked the AI roadmap without ripping anything out."
SCENARIO 02
The project keeps stalling. Vendors have changed twice. Cost estimates keep climbing. The board started asking pointed questions last quarter. You need a partner who actually finishes the migration on the original budget.
"We hit the original migration budget. On the original timeline."
SCENARIO 03
Duct-tape integrations for years are now an audit blocker. A blocked audit means blocked enterprise deals. Compliance is escalating for months. You need an API-first foundation built with AI integration services, not another wrapper on the existing mess.
"Integration is no longer a project. It's a capability."
SCENARIO 04
Headcount isn't the problem. The architecture is. Every feature request turns into a six-week expedition, which process mining services can shortcut. You need workflow automation services that free your team without freezing the roadmap, visible by next QBR.
"We shipped twice as fast last quarter, on the same headcount."
SCENARIO 05
Everyone agrees the platform needs to be replaced. Nobody can agree how to do it without a 12-month feature freeze your CFO will not approve. You need a strangler-fig migration, not a rip-and-replace.
"Customers never noticed we modernized."
SCENARIO 06
Vendor support is ending. Compliance regulations are shifting, raising the bar on AI governance services. The talent pool for your current stack is retiring. You cannot wait another two quarters to start the transition.
"We modernized the runtime without modernizing the entire team."
Every row below is something your team has said in a leadership review. We turn it into a fixed move - backed by AI adoption services and an outcome you can defend to your board. No guesswork. No pilot that goes nowhere. Just production.
AI gets the data it needs. Audit gets the lineage it requires.
One data plane. Three workloads. Zero data sprawl.
Ships at measured accuracy. Every time, not just in the demo.
Agents in production. Operating costs measurably down.
Models ship to production. Stay healthy. Get rolled back automatically when they do not.
AI deployment defensible to regulators, auditors, and the board.
Enabled the digital transformation of a translation company, fuelling business growth
Increase in subscriptions
Increase in revenue
Increase in backoffice operational efficiency
Improvement in customer satisfaction survey
Growth in new customer acquisition
Increase in backoffice efficiency
If you recognize the pilot-to-production problem on this list, we have likely shipped a version of the solution before. Each AI enablement use case calibrated to a production metric your CFO actually cares about. Tell us your variant and we will send the closest precedent we have delivered.
80%
of scored pilots reach production. Eval gates, drift monitoring, automated rollback.
100%
of AI data access governed and lineage-tracked. Audit-ready by default.
95%
measured accuracy. Hybrid retrieval, reranking, hallucination detection.
40%
of cases fully automated business process automation services. Zero hallucination escapes. ROI measured per workflow.
1
data plane for analytics, ML training, and AI inference. Governance built in.
50%
inference cost reduction. Prompt versioning, eval harness, FinOps for AI.
2-week
compliance sign-off (from 18 weeks). EU AI Act and NIST AI RMF aligned.
90%
Drift detected in hours, not quarters - real AI model monitoring and observability. Arize / Fiddler / LangSmith integrations.
10×
retrieval recall vs. keyword-only. Pinecone, Weaviate, pgvector, your call.
100%
Models versioned, signed, and rolled back automatically if eval gates fail.
30-60%
inference cost reduction through enterprise AI infrastructure optimization. Caching, batching, routing across model tiers.
O
hallucination escapes in production caught by AI monitoring and alerting. Content moderation, PII redaction, safety nets built in.
A 30-minute working session with a senior AI architect. Not a multi-week diagnostic you’ve probably already been quoted, and not a sales call disguised as a discovery session. You walk away with a scored baseline and a 90-day plan. No deck, no sales pitch, no follow-up loop. Keep it whether you work with us or not.
Every quarter without a plan is another quarter explaining zero pilots in production.
WHAT YOU WALK AWAY WITH
30 minutes · Senior AI architect · Tomorrow's calendar usually has slots.
Most agencies sell one AI architecture pattern and bend every use case to fit it. We pick the pattern that matches your data state, your latency budget, your governance constraints, and your team maturity, and we tell you when we would pick differently. There is no one AI execution approach for production AI, only the one that fits what you're running.
Lakehouse plus governance plus lineage as the foundation, then AI workloads sit on top. The right pattern when data is scattered, governance is weak, or compliance is a blocker. This is where AI data readiness services start. Production AI without this layer fails compliance review.
CI/CD for models, eval gates, model registry, drift monitoring, automated rollback. The right pattern when models work in the lab and fail in production. Without this, every model in production is a future incident.
AI agents with tool-use, memory, RAG grounding, guardrails, and human-in-the-loop escalation guided with our intelligent automation services. The right pattern for ops, support, finance, and back-office workflows where AI replaces lookups, not judgment. ROI measured per workflow.
Each AI enablement stage has a clear objective, named deliverables, and a methodology you can audit. Our AI operations enablement is not a locked-in contract. Milestone-billed per phase. No commitment beyond what is signed. 30-day exit at any phase boundary, with code, models, evals, and runbooks always in your hands.
01
Objective
Diagnose the pilot graveyard. Score the path to production.
Deliverables
Artifacts you keep, regardless of next steps.
How we work
Discipline visible from day one.
02
Objective
Ship the highest-value AI use case to production.
Deliverables
Production-grade AI, not lab notebooks.
How we work
Demos against milestones, or it is free.
03
Objective
Roll the playbook with enterprise AI scale consulting.
Deliverables
Operational AI maturity, not just demos.
How we work
Hand off as fast as you can absorb.
AI at production scale is the riskiest engineering decision on your 2026 roadmap. Hallucination, drift, compliance, inference cost overrun, team capability gap. Six clauses are written into every Unified AI enablement engagement so each risk is mitigated contractually, not aspirationally, and active from Phase 1 kickoff.
Mitigation: Eval harness gates every release. Hallucination detection and content moderation in the inference path. Human-in-the-loop escalation on ambiguous cases. Zero hallucination escapes is a measured KPI, not an aspiration.
Mitigation: Drift monitoring on input distribution, prediction distribution, and retrieval quality. Automated rollback to last-known-good when eval gates fail. Tooling: Arize, Fiddler, LangSmith, or open-source equivalents based on your stack.
Mitigation: FinOps for AI built in: caching, batching, model routing across cost tiers, prompt compression, cost dashboards per workflow. Cost-per-task tracked alongside accuracy, never separately. Inference budget alarms wired to leadership.
Mitigation: Responsible AI governance framework delivered before Phase 1. Mapped to EU AI Act, NIST AI RMF, HIPAA, FedRAMP. Audit trail, explainability, and human escalation built in. Compliance review embedded in every release.
Mitigation: Data lineage and governance are part of Phase 1, not an afterthought. AI-ready data foundation built before any model ships. Gaps surfaced in the AI Readiness Scorecard and addressed by Phase 1, not Phase 3.
Mitigation: Knowledge transfer baked into every phase. Pair programming and pair-MLOps as default. Runbooks generated continuously. 30-day exit clause active throughout, with hiring plan, role taxonomy, and onboarding kit handed over.
Scaling AI requires three foundations: an AI-ready data layer (governed, lineage-tracked, accessible to models), an MLOps and LLMOps stack (pipelines, evals, monitoring, rollback), and a responsible AI governance layer (audit trails, guardrails, escalation paths). Unified builds all three in milestone-billed phases, starting with a 3-week AI Readiness Scorecard that benchmarks current production gaps and produces a 90-day plan to close them.
MLOps is the discipline of operating machine learning models in production with the same rigor that software engineering applies to applications. It covers CI/CD for ML, model registries, automated evals, monitoring for drift and performance, and rollback when models degrade. Without MLOps, models work in the lab and silently fail in production. Most enterprise AI projects stall at this layer.
MLOps is the broader discipline of operating any ML model in production. LLMOps is the subset that handles Large Language Models specifically, with extra concerns: prompt versioning, eval harnesses for generated text, hallucination detection, retrieval quality monitoring (for RAG), and cost monitoring (because LLM inference costs can balloon). Unified delivers both, layered so the LLMOps practice extends a unified MLOps backbone.
AI operationalization cost varies by data state, compliance requirements, and the number of models in scope. The 3-week AI Readiness Scorecard is fixed-scope and produces a phased cost-and-time band. Senior-led AI enablement is delivered at approximately 50% of premium boutique rates. FinOps for AI is built into every engagement so model inference cost stays predictable. Proposal sent within 48 hours of your first conversation.
The 3-week AI Readiness Scorecard produces the roadmap. The first model in production typically takes 8 to 16 weeks from kickoff, depending on data state. Continuous AI enablement across remaining workloads runs 4 to 12 months. The bottleneck is rarely the model itself. It is the data foundation, the eval harness, and the governance sign-off.
Responsible AI governance is the policy, process, and tooling that makes AI deployment defensible to regulators, auditors, and customers. It includes audit trails, bias testing, explainability, human-in-the-loop escalation paths, and content moderation. In regulated industries (financial services, healthcare, government), it is increasingly required by law (EU AI Act, NIST AI RMF). Even in unregulated industries, it is required by customers and boards. We deliver a governance framework as part of every AI enablement engagement.
RAG (Retrieval-Augmented Generation) grounds LLM responses in your private knowledge so the model answers from your data instead of hallucinating. Production-grade RAG requires more than vector search: hybrid retrieval (semantic plus keyword), reranking, eval harnesses, query optimization, freshness pipelines, and a feedback loop. Most RAG demos collapse in production because none of this is wired up. We build RAG for production from day one.
Model monitoring is multi-layered: input distribution drift, prediction distribution drift, feedback signal monitoring, and cost monitoring. When any signal degrades beyond threshold, alerts fire, the eval harness re-runs, and an automated rollback to the last-known-good model is available. We deliver this monitoring stack in MLOps engagements with tools like Arize, Fiddler, LangSmith, or open-source equivalents based on your stack.
Years Modernizing Platforms
Global Clients
Tech Experts
Tell us about your AI pilots and what is blocking production. A senior AI architect, the same person who would run your engagement, sends back a tailored AI enablement proposal with phased options. Typically within 48 hours.
NDA on request. The AI Readiness Scorecard outline is yours to keep, with or without engagement. No follow-up sales call required.
