AI workflow & automation — for businesses and agencies worldwide

AI workflows that take the busywork off your team — and keep a human where it matters.

Multi-step AI agents, document pipelines, triage and routing, research automation — engineered with the guardrails, tracing, and evaluation that make them safe to run unsupervised. We automate the judgment, leave the deterministic work to rules, and put a person at every step that genuinely needs one.

Start an AI workflow project See workflows we have shipped

1,000⁺

Engineering projects shipped since 2015

10yrs

Engineering tradition behind the AI work

4.9★

Across 1,000+ reviews

100%

Of agent actions traced · every workflow

The real cost

An AI agent is easy to start and dangerous to leave unguarded.

An agent that can take actions is genuinely powerful — and an agent that can take actions without guardrails is a liability waiting to happen. The difference between a workflow that saves your team real time and one that quietly causes a mess is engineering most teams skip. The three observations below are what we say out loud on every AI workflow discovery call.

An agent with no guardrails will eventually do something you did not authorize.

The thing that makes an AI agent useful — that it can take actions, not just produce text — is also the thing that makes it risky. Give an agent a tool that can send an email, delete a record, move money, or publish something, and enough autonomy to use it, and it can eventually follow its own reasoning to a place you never anticipated. Not because the model is malicious, but because it is probabilistic and you cannot enumerate every path it might take. An agent without hard guardrails on its dangerous tools is not a productivity gain — it is an incident waiting for a quiet afternoon.

The hard part of automation is the edge cases, not the happy path.

Any automation works on the happy path — the clean input, the expected document, the request shaped the way the demo assumed. That is maybe a fifth of the real work. The other four-fifths is the long tail: the malformed file, the half-finished request, the tool that timed out, the input nobody thought to test. A workflow that handles only the happy path does not save your team time; it shifts their time from doing the work to cleaning up after the automation. The engineering that matters — and the engineering most teams skip — is the tail, not the demo.

An agent you cannot audit is an agent you cannot trust.

When an agent produces a wrong outcome, "the agent got it wrong" is not a diagnosis — it is a shrug. Without a full trace of every step the agent took — every decision, every tool call, every result it saw — a failure is unexplainable, and an unexplainable failure is unfixable. Worse, an untraced agent gives you no way to know it is going wrong until the damage is visible. Observability is not a nice-to-have on an agent; it is the thing that lets you trust an autonomous system with real work, because you can always answer the question: what exactly did it do, and why?

What we engineer

Six kinds of AI workflow, each engineered to run safely.

Multi-step AI agents

Agents that take a goal and complete it — planning, calling tools, observing results, looping until done. Step and cost limits, validated tool inputs and outputs, human approval before anything irreversible, and full tracing of every action. Autonomous on the safe steps, supervised on the rest.

Document processing pipelines

Turning inboxes of unstructured documents — invoices, contracts, forms, applications — into structured, validated data. Extraction with typed outputs, confidence scoring, exceptions routed to a person, and the deterministic steps kept as rules. The four-fifths of the work that is the messy tail, handled.

Triage & routing automation

Reading inbound requests — tickets, emails, leads, applications — understanding what they are about, and routing them to the right team, queue, or owner. Classification with typed outputs, measured against a labelled set, with low-confidence cases sent to a person rather than guessed.

Research & summarisation agents

Agents that gather information across sources, synthesise it, and return a structured, cited summary — market scans, competitor monitoring, candidate research, account briefings. The reading-and-summarising work a person does for an hour, done in minutes, with sources you can check.

Tool & system integration

The tools an agent acts through — connections to your CRM, your database, your APIs, your internal systems — built as validated, typed functions with clear boundaries. An agent is only as capable, and only as safe, as the tools you give it; this is where that is decided.

Human-in-the-loop & approval workflows

The control layer that makes autonomy safe. Agents run unsupervised on low-stakes, reversible steps and pause for human approval before anything irreversible — sending, paying, deleting, committing. Clear review interfaces, full context for the approver, and a logged decision every time.

Beyond the build

The work that keeps AI working after launch.

An AI system is only as good as the last week of evaluation, the last model swap, and the last round of cost tuning. Three engagement types alongside the build itself.

Evaluation & continuous improvement

Golden datasets that grow every week. Judge-model evaluation on every prompt change. CI-gated accuracy floors. Production failure capture fed back into the golden set. Weekly eval-coverage report. Most agencies treat evals as a one-off; in production they are a continuous engineering practice.

Golden dataset curation & weekly growth
Judge-model pipelines (Promptfoo, custom)
CI-gated accuracy floor enforcement
Eval coverage report · monthly review

Model swaps & cost optimisation

Model providers ship cheaper, faster, more accurate models every quarter. Without a swap discipline, you lose 30 to 60% on cost annually by staying on yesterday's frontier model. We A/B every promising new release on your real workload, swap when the eval data justifies it, and keep the inference cost trending down.

Quarterly model A/B against your workload
Inference gateway with multi-provider routing
Prompt caching & batching where supported
Cost per task tracked, reviewed monthly

AI-readiness audits for existing products

Where in your existing product would AI actually pay back, and where would it just be a feature nobody asked for? We audit existing applications against an eight-point AI-readiness framework and return a prioritised roadmap of AI integrations with cost, time, and expected impact ranges — not a slide deck pitching AI features.

Eight-point AI-readiness audit on your product
Prioritised AI roadmap with cost / time / impact
Honest reading of what AI will and will not move
2 to 4 week engagement, fixed cost, written deliverable

AI reliability & performance scoreboard

The numbers every AI build we ship has to hit.

Every AI build is shipped against four hard targets, named in the contract before sprint one. We measure them in production, we tune them every week, and the system does not ship until each one is in the green.

01 — Inference latency budget

p95 under the contracted budget · per use case

Different AI use cases tolerate different latency. A real-time chat copilot needs p95 under 1.5 seconds; a batch document processor can tolerate 30 seconds. We name the latency budget per use case in the contract, build to it with smaller-model fallback paths and aggressive caching, and track p95 in production every day.

02 — Cost per inference

Cost per task within the contracted ceiling

Cheap path for the easy 80% (smaller model, cache hits, batched inference). Expensive path only when the small model returns low confidence. Cost per task tracked daily; monthly review against the budget. Most AI projects underspend by 30 to 50% versus the initial estimate because the typical first build is over-engineered on model choice.

03 — Accuracy & eval coverage

80%+ eval coverage · golden dataset growing weekly

Golden dataset of 200 to 1,000 examples per use case, curated with the client, tracked in production failures, growing every week. Judge models score every prompt change and every model swap against the golden set. CI fails the build when accuracy drops below the contracted threshold.

04 — Hallucination rate & safety

Hallucination ceiling named in the contract

Hallucination rate ceiling agreed in the contract per use case (typical 0.5 to 2% depending on stakes). RAG-grounded responses with judge-model verification. Constrained outputs via structured response schemas. Human-in-the-loop on low-confidence cases. Safety filtering on user inputs and model outputs.

How we build AI

Five steps from brief to AI you can rely on in production for a decade.

The process below has held for every AI engagement we have shipped. Every step is required. Skipping any one of them is how AI ends up running in production unmeasured, expensive, and quietly drifting toward failure.

Discovery and feasibility

We learn the use case, the audience, the existing system if any, and the goal numbers (accuracy threshold, latency budget, cost ceiling, hallucination tolerance). We finish with a written brief and an honest reading of which parts are feasible with AI today, which parts are feasible with engineering effort, and which parts are not feasible yet. AI feasibility is the most important conversation we have on every project, and the one most agencies skip.

Architecture and eval design

System architecture: model selection per critical path, retrieval architecture if RAG, agentic patterns if tool use, fallback paths, observability. Eval design: what does success mean for this use case, what does the golden dataset look like, what is the judge model, what is the CI-gated accuracy floor. The architecture and the evaluation framework are decided before any feature work starts.

Build with evals from day one

Build the AI capability and the evaluation harness in the same sprint, not sequentially. Every prompt change, every model swap, every retrieval-tuning change runs against the golden set automatically. Two-week sprints with eval reports at each demo. Production observability live from sprint one, not retrofitted at the end.

Eval-gated rollout

Staged rollout behind a feature flag. 5% to 25% to 100% over two weeks. Eval coverage and production hallucination rate watched in real time during ramp. Rollback on any of the four SLOs (latency, cost, accuracy, hallucination) breaching the contracted ceiling. We do not declare launched until the system has held its SLOs for seven consecutive days at full traffic.

Production observability and continuous improvement

Sentry for application errors. Langfuse or Helicone for inference observability. Custom dashboards for the four SLOs. Weekly golden-dataset growth from production failures. Monthly model A/B against the workload. Quarterly architecture review. Most AI projects degrade silently in production without this cadence; ours improve every quarter.

Selected AI work

Two real shipped AI systems · four representative engagement patterns.

The first two cards are real, shipped, in production. The remaining four are representative of the engagement shapes we run most often — anonymised where the client name is sensitive. We are honest about which is which on the discovery call.

Heritage Reports

Manual → fully dynamic · reports at scale

Generative AI · OpenAI LLM · family heritage (REAL)

dsflow.cloud

Built end-to-end with AI engineering tooling

AI-built PMS · Claude Code + Cowork (REAL)

Meridian Intelligence

p95 1.4s · 0.4% hallucination · 38k calls/day

RAG over internal docs · engagement pattern

Frondhill Triage

78% of tickets auto-routed · human-in-loop on the rest

Agentic ops automation · engagement pattern

Postbrew Recommend

+22% reorder · A/B vs static recs

In-product AI feature · engagement pattern

Aurora Semantic Search

10× recall vs keyword · 380ms p95

Semantic search over content · engagement pattern

Need AI that actually works in production?

Send us a brief about the use case, the goal numbers (latency, cost, accuracy, hallucination tolerance), and the data. We come back with a written plan — architecture, evals, cost estimate, no slides.

Request an AI discovery call

Where AI shows up

Four AI surface types, one engineering team behind them.

The same AI engineering capability adapts to four very different problem shapes. Visual language stays consistent; what changes is the architecture, the eval design, the cost model, and the latency budget.

RAG over your documents

Retrieval-augmented generation

Internal knowledge base search, documentation assistants, customer-support copilots over your own articles, sales-enablement copilots over playbooks. The most common production AI pattern in 2026 — and the one most worth getting the architecture right on.

Agentic process automation

Multi-step AI agents

Ticket triage, document processing, research assistants, deal-room analysis. Tool-using agents that complete work end-to-end, with human-in-the-loop on cases the system marks as low-confidence. Cheap on the easy 80% of tickets, accurate on the hard 20%.

In-product AI features

AI features inside your existing product

Smart search, summarisation, classification, recommendation engines, generative drafting, in-context copilots. Feature-flagged rollout, A/B against the non-AI path. Your product stays your product; AI becomes one capability inside it.

Custom internal AI

Internal AI tooling

Drafting tools, document analysers, triage assistants, custom research bots for your team. Lower-stakes than customer-facing AI, faster to ship, easier to experiment. Most companies have ten internal AI use cases worth building before they have one customer-facing one ready.

Client stories

Two real AI engagements, and what changed for the businesses behind them.

Heritage Reports — family-history dynamic narrative system

Generative AI · OpenAI LLM · 2024

The situation

A family-history research company producing reports manually — research, narrative writing, and crest design done by hand by a small team. The process was labour-intensive and capped how many reports the business could ship per month. The team had genealogy data and the editorial standards; what they did not have was a way to scale narrative generation without losing quality.

What we built

OpenAI LLM-powered dynamic generation system. The client's structured genealogy data flows through the system; narratives, design assets, and crest concepts generate automatically against the editorial standards the team encoded as prompts and rubrics. Quality checks layered on top — judge-model verification for narrative consistency, automated fact-checking against the source data, human review for final sign-off on the parts that warrant it.

The outcome

Manual report process eliminated. Client now ships fully dynamic, personalised reports at scale. The editorial standards that used to live in the heads of two researchers are now encoded in the system — new team members produce work at the same standard from day one. Volume and unit economics both moved in the right direction; the business is now growth-constrained rather than capacity-constrained.

Talk to us about a generative AI build →

dsflow.cloud — AI-built project management tool

Internal AI engineering reference · Claude Code + Cowork · 2026

The situation

Vikas Upadhyay, our founder, wanted a project management tool built around how Dream Steps actually runs engagements — not the generic shape every PMS tool ships with. The team needed a real product to use, not a demo. The build needed to be a credibility reference for our own AI engineering capability — "we use the tools we recommend" rather than a slide claiming AI fluency.

What we did

Built end-to-end with Claude Code, Cowork mode, and Claude sub-agents. AI-assisted across architecture decisions, schema design, frontend implementation, refactoring, debugging, and documentation. Engineers in the loop on every decision; AI accelerating the work, not replacing the judgment. The product itself is a working PMS in production at dsflow.cloud, used internally and being prepared for external launch.

The outcome

A real product running on AI-assisted engineering. Demonstrates Dream Steps building production software using AI as a first-class engineering tool, not as a marketing claim. Internal proof point for the AI-era positioning and a reference for clients asking what AI-built actually looks like. The build cadence on dsflow.cloud is the reference cadence we expect on AI-assisted engagements with clients.

See dsflow.cloud →

For agencies & product teams

The AI engineering team behind the agency.

Roughly 35% of our AI work is built for other agencies, product studios, and consultancies — under their brand, against their clients' deadlines. Three partnership models, all NDA-protected, with senior AI engineers working in time zones overlapping the UK, EU, and US workday.

01 · Partnership model

White-label AI engineering

Your brand. Our engineers. We never appear in front of your client — all communication, deliverables, and code go out under your name. The standard model for agencies that win AI projects but do not want to hire in-house AI engineering.

NDA & sub-contract in place before any work begins
Code, design files, and deliverables shipped under your brand
Joint Slack / email channels with your team only
You stay client-facing; we stay implementation-facing

Used by: digital agencies, full-service shops, consultancies

02 · Partnership model

Agency-of-record & dedicated AI pod

A pod of senior AI engineers and a project lead working as your in-house Laravel capacity — full-time or fractional, month-to-month or annual. The choice when AI is core to your service mix and hiring in-house is slower or more expensive than partnering.

Dedicated pod: 2 to 6 engineers + lead, scaled to your roadmap
Direct integration into your project tools (Jira, Linear, ClickUp, Asana)
Monthly capacity commitment; retainer or rolling SoW
Code ownership transferred to your repos

Used by: full-service agencies, SaaS product teams

03 · Partnership model

Capacity overflow & sprint-by-sprint

When your in-house AI team is full and the next project cannot wait. Sprint-by-sprint engagement, no commitment beyond the current two-week sprint, ready to pick up scoped work within 5 to 7 business days from green-light.

Two-week minimum sprint, rolling renewal
Scoped fixed-price work — feature build, migration, performance pass
Fast spin-up: 5 to 7 business days from signed SoW
No long-term commitment; ramp up or down per sprint

Used by: agencies with seasonal AI demand spikes

NDA-protectedStandard NDA, sub-contract, and IP transfer in place before any work begins.

Time-zone overlapWorking hours overlap with UK mornings, EU workday, and US afternoons every business day.

Single point of contactNamed project lead on every engagement. No agency-side account churn.

Your repos, your codeCode ownership transfers cleanly. We work in your Git, your hosting, your tooling.

Already running an agency or product team? Explore our white-label terms Start a partner conversation

From the journal

More on shipping AI that actually works.

Working notes from inside our AI engineering practice — written for founders, product leads, and engineering managers making decisions about RAG architectures, evaluation pipelines, and AI in production.

AI Engineering · Coming soon

Building a production RAG system — the architecture, the evals, the cost

The full architecture of a production RAG system: chunking strategy, retrieval, grounding, judge-model verification, observability, cost ceilings.

Read the article

AI Engineering · Coming soon

The half of AI engineering nobody talks about — evals, golden datasets, judge models

What an actually-useful evaluation pipeline looks like in 2026, why most teams skip it, and how to build one from scratch in a fortnight.

Read the article

AI Engineering · Coming soon

The seven AI cost levers we pull on every production build

Smaller-model routing, prompt caching, retrieval-budget tuning, batching, structured outputs, async-where-possible, and observability that lets you tune any of these without flying blind.

Read the article

All articles

Why not

Generic agencies talking AI, demo-driven AI startups, and self-built prototypes vs proper AI engineering.

Three routes most teams consider before they hire a real AI engineering partner. Each makes sense for someone. None hold up in production the way properly engineered AI does.

Generic agency claiming AI

One model, one prompt template, one architecture for everything
No evaluation harness · "the model feels good"
No observability · failures surface as customer complaints
Cost is a surprise · budgets blow out in month two
Brittle in production · rebuilt within a year

Demo-driven AI startup

Impressive demos on cherry-picked inputs
Small team, founder-driven, hard to scale onto your roadmap
Domain expertise weaker than agencies with 10+ years of delivery
Few production-grade case studies past the demo stage
Often the wrong fit for non-AI parts of your build

AI engineering at Dream Steps

Model-agnostic · Claude / GPT-4o / Llama as the case fits
Eval-driven from sprint one · CI-gated accuracy floor
Production observability · latency, cost, hallucinations tracked daily
Cost engineered, not estimated · named cost per task in contract
10-year engineering tradition behind the AI work

Most agencies talking about AI in 2026 have not shipped production AI.

The market is full of pitch decks claiming AI capability that, on inspection, is a single integration with one model that the team has not measured rigorously. We have shipped production AI for clients. We use AI engineering tooling daily across our team — Claude Code, Cowork, OpenAI tools in our own builds. We are building deeper AI capability every month. Most agencies you talk to about AI cannot honestly say all three. That gap is the credibility difference, and it is widening every quarter.

AI-only startups will demo well; they often cannot ship the system around the AI.

An impressive demo and a working production system are two different engineering challenges. The AI-only team builds the part that demos well; you still need the rest of the application, the observability, the integrations, the deployment story, the security review, the accessibility audit, the maintenance cadence past launch. The case for a team that has shipped 1,000+ engineering projects across a decade is that the system around the AI does not get forgotten. AI is one capability inside a real product.

Self-built AI prototypes are excellent — until the day they need to ship.

Most teams build their first AI prototype themselves. That is the right call. The prototype proves the use case, gets internal alignment, and informs the architecture. The moment that prototype needs to ship to real users at real scale is the moment evaluation, observability, cost control, latency budgeting, and production hardening become the bottleneck. That is the moment we typically come in. The first paid engagement is rarely a greenfield AI build; it is more often the path from working prototype to production-grade AI system.

— The honest read

Build AI that holds up in production, not AI that demos well in a meeting.

Request an AI engagement

Common questions

Questions AI buyers actually ask.

Fourteen of the most common AI engineering questions, answered straight. If yours is not below, send it and we will reply with a real answer — not a sales pitch.

Why choose Dream Steps for AI workflow automation?

We build AI workflows as engineering, not demos — with guardrails, human-in-the-loop checkpoints, full tracing, and evaluation on whole-task outcomes. Every workflow is shipped against four named SLOs: task completion rate, cost per task, accuracy on a golden set, and a verifiable safety standard. We are a 40-person engineering team in Noida, India with a ten-year engineering tradition behind the AI work, and we build with AI tooling ourselves — dsflow.cloud, our own project-management product, was built end to end this way. We are also honest about which parts of a process should not be automated at all.

Can you white-label AI workflow development for our agency?

Yes. A significant share of our AI work is built for other agencies and consultancies under NDA. Three partnership models: white-label (your brand, our engineers, fully invisible), agency-of-record (a dedicated AI pod working as your in-house capacity), and capacity overflow (sprint-by-sprint engagement when your team is full). Code ownership transfers to your repositories, and we run inside your tooling as standard. Agents and automation are exactly the kind of work clients are now asking for and agencies are least often staffed to deliver safely.

Where is your AI team based?

Our entire team is based in Noida, India — 40 people in our iThum Tower B office, founded in 2015. We work with businesses and agencies across the UK, US, Ireland, Australia, the UAE, Germany, and the Netherlands. Working hours overlap with UK mornings, the full EU workday, and US afternoons. For agency partners we run in their tooling as standard, and every engagement has a named project lead as a single point of contact.

Should my workflow use an AI agent or traditional automation?

Usually both. Traditional automation handles the deterministic, structured steps — faster, cheaper, and perfectly reliable. An AI agent handles the steps that need judgment over messy input. The strongest design is a rule-based workflow with an agent dropped in only at the specific points that genuinely need a model. Handing an entire process to one agent is slower, costlier, and harder to trust than it needs to be. We map the workflow and tell you which steps want rules, which want an agent, and which want a person.

How much does an AI workflow cost?

AI workflows range from a single focused agent or pipeline through to a process-wide automation touching several systems. The scope drivers are the number of steps automated, the number of tools and systems the agent must integrate with, the messiness of the inputs, and the accuracy and safety bar. We scope every engagement against the specific brief, are competitive with established engineering rates internationally, and are honest about which steps to automate first. A process audit is the lowest-commitment way to get a costed roadmap.

How long does it take to build an AI workflow?

A focused agent or document pipeline is typically an 8 to 14 week engagement, depending on how many tools it integrates with and how high the reliability bar must be. A process audit is 2 to 4 weeks. We work in two-week sprints with weekly demos, and because the eval harness and guardrails are built first, the completion-rate number is visible the whole way through. More complex multi-step workflows take longer, and we phase them so the highest-value steps ship first.

Are AI agents reliable enough to run unsupervised?

On the right steps, yes — and that is the key qualification. A well-engineered agent runs unsupervised on low-stakes, reversible steps and pauses for human approval before anything irreversible. Reliability comes from the engineering around the loop: step and cost limits, validated tools, guardrails, full tracing, and evaluation on whole-task outcomes. An agent with that is safe to leave running on the work it owns; an agent without it is not. We design every workflow so the autonomous parts are genuinely safe and the risky parts always reach a person.

How do you keep an AI agent from doing something harmful?

With guardrails designed in from the start. The agent is given a limited, deliberately chosen set of tools, so it cannot act outside that set. Dangerous and irreversible actions — sending external messages, moving money, deleting data — are gated behind a hard requirement for human approval. Step and cost limits stop a runaway loop. Every action is traced and reviewable. The safety of an agent comes from these engineered controls, not from trusting the model to always behave — and we treat that safety standard as a named, verifiable SLO.

What does "human-in-the-loop" actually mean?

It means the agent runs autonomously on the steps that are safe and reversible, and pauses for a person to approve anything that is not — sending, paying, deleting, committing. It is a deliberate design feature, not a limitation. A good human-in-the-loop workflow gives the approver full context and a clear interface, so the review takes seconds, and it logs every decision. The aim is not an agent that never needs a human; it is an agent that asks for one at exactly the right moments and handles everything else on its own.

Can you automate a process inside our existing systems?

Yes — that is the normal case. An AI workflow acts through tools, and those tools are connections to your existing systems: your CRM, your database, your ticketing system, your APIs. We build those connections as validated, typed functions with clear boundaries, so the agent works inside the systems you already run rather than replacing them. Your systems stay your systems; the workflow adds an engineered layer that reads from and acts on them, with guardrails on anything that changes state.

What should we not automate with AI?

Three kinds of work should stay with a person. High-stakes, irreversible decisions — sending money, legal commitments, final hiring calls. Anything where an error would be expensive and hard to catch, because nothing would be checking it. And work that needs genuine accountability or empathy. AI can assist all of these — drafting, suggesting, surfacing options — but it should not own them. Deterministic, structured steps should also not go to AI: rules do them better. We are honest about this on every engagement, because automating the wrong thing is the most common AI workflow mistake.

How do you measure whether an AI workflow is working?

On whole-task outcomes, not single steps. We build an evaluation set of real tasks with known-good results and score the workflow against it on every change, with a CI gate that blocks releases below the completion and accuracy floors. In production we track task completion rate, cost per task, and the safety standard, and we capture every failure and feed it back into the eval set. The result is a workflow that gets measurably more reliable every month — rather than one that quietly drifts and nobody notices until it matters.

Will you maintain the AI workflow after launch?

Yes, and AI workflows need it. They run every day, take actions, and meet new inputs constantly. We offer ongoing engagements covering monitoring for runaway loops and cost spikes, eval-set growth from real production failures, guardrail review as the workflow’s tools and scope change, model A/Bs and swaps, and dashboards for completion rate, cost, accuracy, and safety. A running agent is a system that needs tending — not a build you walk away from.

What is the difference between AI Integration, AI Workflow, and AI Product?

AI Integration is adding AI features to an existing product — RAG, search, copilots. AI Workflow — this page — is automating a process with AI, including multi-step agents that take actions across your systems. AI Product is building a product where AI is the core, not an add-on. They share the same engineering foundation — tools, evals, guardrails, observability — and many engagements touch more than one. Our AI Engineering hub page is the place that ties all three together.

Ready when you are

Automate the workflow so well you can stop thinking about it.

Tell us about the process — the steps, the systems, the time it eats. We will map it step by step, come back with a written plan of which steps to automate and how, and the SLOs we would commit to — plus an honest read on which steps should stay with rules or with people.

Request an AI discovery call See more of our work

What to expect

A 30-minute conversation about the use case, the data, the goal numbers, and what the production system has to do at scale. No slide deck, no pitch.

You walk away with

A written plan naming the architecture we recommend, the evaluation framework, the four production SLOs we will hold ourselves to, the timeline, and a realistic build cost.