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Artificial Intelligence at Psiog

We make AI work for your business

Psiog designs, builds and runs AI systems for mid-market enterprises. Every AI initiative is anchored to a business lever it has to move - such as margin, forecasting accuracy, risk compression or decision velocity.


Four questions decide the approach: what value is expected, how much reasoning to embed, where the advantage sits, and what the solution is built from. We prove the uplift against a baseline we build with you.

Adoption 100% of our Top 10 customers have a sponsored AI initiative or an internal POC running with us
Speed to value 3–4 weeks from a scoped problem to a working, validated solution
Be-spoke AIDLC harness >50% acceleration in Engineering, for Hi-Tech & Outsourced Product development
Ontology & agents in action Up to 5x faster M×N data integration for RCM & Healthcare industries

Point of view

What you commonly hear about AI, and what we think

The market is loud right now. Most of what gets said about enterprise AI is directionally true, but practically unhelpful.

What we think

Square hammer for a round hole. Most workflows do not need AI as the first thing. On a recent data platform support engagement, every candidate workflow went through an intake asking one question - rule-based, or AI required. All but a few came back rule-based. The discipline is knowing which problems reward reasoning and which do not.

What we think

Start with a business KPI somebody owns. Most AI business cases are argued against a baseline that doesn't exist or was never measured. We build the measurement apparatus before we build the solution, so the uplift claim arrives as a comparison against a number we both agreed on.

What we think

How much reasoning to embed is the primary architecture decision. Our operating rule is - cheapest reasoning first, escalating only when the simpler method cannot resolve it. In a production mapping pipeline - that means alias matching, then semantic matching, then ensemble methods, and only then a model. Analyst reports suggest that roughly 40% of agentic AI projects will be cancelled by 2027. Over-reasoned systems are a meaningful share of that number.

What we think

Framing a business problem and building the answer pull in different directions. Held by one person, the urgent half wins and the other gets done later. We run it as a pair. Out 'Business Technology' team frames the problem and owns the business decisions, the 'Technology Advisory Council' team owns feasibility and the build, and both stay for the whole engagement rather than handing off at a gate.

What we think

Autonomy is the new integration problem. Agents span silos, chain tool calls and adapt at runtime. Somebody has to set capability boundaries, standardise interaction patterns and make guardrails enforceable across an estate. That is integration work, and there is more of it than before.

Our approach

The core dimension

Embedded Reasoning

Designing the right degree of reasoning into a solution, not the maximum and not the minimum, is the primary AI architecture decision. It determines what is automated, how, at what depth of intelligence, and at what risk.

Less reasoningMore reasoning

The value ladder

Cost savings→ KPI-linked→ P&L attribution→ Real-time ROI

Most AI programmes report at the first rung, which is why boards stay unconvinced about AI investments. Cost savings are real but easy to dispute, because the work might have got cheaper anyway. The RoI rung you can honestly claim depends on whether anybody built a baseline before the work started. That decision gets made in Week 1.

Two modes, chosen deliberately

AI-enabled↔ AI-first

Intelligence added to a system that works, or intelligence as the system itself. Most engagements are the first. The comparison is elaborated below.

Where the advantage sits

Process-centric↔ Platform-centric

AI inside the delivery lifecycle itself, or AI inside the products you already run. We treat each vendor's agentic surface as a delivery layer in its own right.

What an enterprise AI solution is made of

Agentic orchestration Intelligence layer Guardrails

Guardrails are an architectural element, and should not be a compliance afterthought. Input validation, observability, resilience, agent configuration and human gates need to be designed in from the first sprint.

Anchored to a business lever, or funded as a technology exercise

We anchor each initiative to one of six levers at the outset. The lever decides how value will be measured, which in turn decides how far up the value ladder the programme can honestly report.

Revenue expansion

New capability, faster time to market, pricing precision.

Margin improvement

Effort removed from work that was never differentiating.

Asset utilisation

More output from systems and people already paid for.

Forecasting accuracy

Forecasts built on current demand signal.

Risk compression

Failures caught early, audit trails that survive scrutiny.

Decision velocity

The interval between a question being asked and answered.

If an initiative maps to none of the six, it is a technology exercise and should be funded as one.

AI in two modes

AI-enabled and AI-first

Mode 01

AI-enabled

Intelligence added to a system that already works

Mode 02

AI-first

The intelligence is the system, not a feature of it

What carries the workflow
Deterministic logic. AI takes specific manual tasks at the edges.
Agents, orchestration and reasoning at the core.
Use when
The process exists and works. Integration with legacy systems is central. The decision logic must stay auditable.
The workflow is novel or reasoning-heavy. Inputs are unstructured and variable. Learning over time is a requirement, not a bonus.
In practice
Three-way invoice matching. Agents handle email ingestion and invoice extraction at the edges while the match itself stays deterministic.
A data-exchange pipeline. More than 25 specialised agents absorb files arriving in a different shape from every client, and every mapping decision persists.
What that buys
Over 60% fewer manual reconciliations, every decision traced end to end in Azure Monitor, and nothing posts to the ERP without a three-way match.
Analysts author rules in plain language with no engineering backlog, and recurring clients resolve from a warm cache with near-zero model calls.
The deciding factor
Risk appetite before architecture. Chosen where readiness is partial, the process is mission-critical, or the decision logic has to stand up to audit.
Chosen where the organisation can carry more risk on the use case, and the workflow is new enough that there is no deterministic version to protect.

AI is not just a service line. It is a layer through the services you already buy.

Most engagements are AI-enabled first, AI-first later. AI-first is where the more interesting architecture lives, and it is a smaller share of the work than the market noise suggests.

What we offer

ENVISION AI Assessment & Workshop ENABLE Data for AI MODERNIZE Legacy & Application Modernization AUTOMATE Enterprise Automation ASSURE GOVERNANCE & TRUST through all four

Select a quadrant

Envision · AI Assessment & Workshop

Discovery led AI strategy and architecture. Where AI belongs in the business, on what platform, and under whose ownership. Scattered AI ambition turned into a sequenced, funded roadmap.

Output
  • Target AI operating model
  • Prioritised use-case portfolio
  • Delivery roadmap

Enable · Data for AI

Enterprise data, structured and unstructured, made AI ready. Platforms, integrations and engineering that turn raw data into an asset an agent can be trusted to act on.

Where source data is messy, mapping and reconciliation come first. That is the readiness work that makes everything above it credible. Skipping it is a common reason an AI programme stalls.

Modernize · Legacy Modernization & Application Engineering

AI used to understand the intent buried in an existing estate, then modernise it progressively, without disruption. Two routes, depending on what the client is trying to move.

Two routes
  • Accelerating engineering work. AI inside the delivery process itself, human-in-the-loop, moving throughput and cycle time as a business KPI. System-specific today, cross-system in build.
  • Enhancing products with AI. Re-architecting the client's own product so AI is native to it, making what they sell more marketable.

Automate · Enterprise Automation via Agents

Core business processes redesigned around domain aware agents working alongside people.

Efficiency and experience measured on the business outcome - scoped to a redesigned end-to-end workflow. Automating the existing one tends to move effort somewhere less visible instead of removing it.

Assurance · Governance and Trust runs through all four

Responsible, secure AI across the lifecycle - Risk assessment, policy, guardrails, cost governance and human-in-the-loop design.

With AI embedded in every engagement today, trust becomes essential. At scale, with hundreds of enterprise agents - governance moves beyond the individual agent and use case to building enterprise context. It is sequenced after the data foundation is in place, not before.

Case studies

Where AI is already running

Enterprise automation 21 August 2026 Invoice Delivery Agent for an EPC-Infrastructure Services Provider View details Vision & validation 7 September 2026 AI for Vision & Image Validation View details Agentic operations 3 August 2026 Agentic ticket resolution for a business-critical five-platform data estate View details Data integration 16 September 2026 Consumer retail EDI integration for SAP View details

Upcoming case studies . .

AIDLC - Harness engineering across the delivery lifecycle

A governance and compliance software firm needed AIDLC adoption across the whole lifecycle, from planning through testing. Phase-specific skills cover planning, architecture decision records, code generation, self, peer and lead review, and testing. Existing quality gates stayed intact, and the harness went through a formal go or no-go checkpoint on a bounded pilot before any decision to scale.

Measured
  • 40% faster product launch
  • 30% engineering uplift from Sprint 1
  • Flow efficiency of >90%
  • >30% consolidated cost saving

Production-grade QA for an agentic platform

Testing a non-deterministic multi-agent codebase, where conventional QA suites broke on every interface change or behaviour shift. Evaluation pipelines exercise multi-step reasoning paths with structured evidence assertions. Tests self heal through accessibility tree and semantic selectors. And deterministic replay keeps external calls out of regression gates.

Outcome
  • 50,000+ lines of code audited daily
  • Models routed by reasoning level, no single-provider lock-in
  • Tests survive routine refactors

Built on

Partnerships

Microsoft Solutions Partner – Data & AI (Azure) Microsoft Solutions Partner – Digital & App Innovation (Azure) Databricks partner Boomi partner Creatio partner

AI platforms and models we build on

Claude by Anthropic
Claude Code
Azure OpenAI
DeepSeek
OpenRouter
LangGraph
MCP
AgentField

Our approach

How we engage

Assessment, discovery, the baseline, the prototype, accelerated delivery, the roadmap, and the roles involved.

Discovery, in two modes of execution

Mode 01

Traditional discovery

Interview-led and analysis-heavy

Mode 02

AI-led discovery

The same architecture, executed with AI in the loop

What you get
Findings, root-cause diagnosis and a roadmap the client then acts on. Value is argued on paper and lands after the engagement ends.
Context assembled faster and the solution shaped while discovery is still running. A working harness leaves with the engagement, so the client watches the value arrive.
How it is priced
On effort and elapsed time.
Discovery to prototype, priced differentially.

The acceleration comes from a harness carrying the context assembly or from the prototype sitting inside discovery. How quickly real inputs reach the harness is the thing that decides the pace.

Two flavours of AI-led discovery

FLAVOUR 0150,000+ lines of code au

The AI workshop

Find the high-impact, high-control initiative and attack a business KPI head-on. More process and people than technology. The deliverable is proven adoption and measured uplift. Works where the KPI is owned, the process is understood, and a pilot team can be named.

FLAVOUR 02

An AI avatar of a traditional discovery

The same discovery, run with AI in the loop, across business intelligence and dashboards, custom applications, product development and low-code platforms, and on data platform work.

The value language shifts by pattern - feature-development acceleration, performance improvement, or a new AI capability. So does our posture, thinking partner on some engagements and execution partner on others.

How we run discovery

Business, processes, then systems, then data. Business outcomes and the application estate shape the data foundation direction. That sequence keeps highlighting the same thing - agentic reporting and downstream AI only work on a trusted, connected, well understood data foundation, so readiness work usually comes before anything agentic is worth building.

Discovery is run from Psiog's tried & tested playbooks. Data, custom and low-code platform playbooks are in use, with pre-discovery, mid-point and post-discovery checklists. Every discovery is then assessed against a standing quality framework:

The problem

Problem diagnosis.
Effort & Risk
Complexity diagnosis.

The answer

Solution coverage, and whether we went beyond the brief. Technical feasibility, Architecture and Integration.

The engagement

Client trust and value adds.
Process and governance.
Delivery readiness.
What went back into the knowledge base.

The AI workshop · attacking one owned KPI

Three parts. Unlike a traditional discovery, the first part closes with something working. Here is a sample methodology illustration for an AIDLC programme engagement for a Hi-Tech Product Engg team

1

Discovery

Choose the use case, define four to five metrics, name the pilot team, and build the harness against the end-to-end process.

Leaves you with
  • A working harness against the end-to-end process
  • A baseline collector and an actuals collector
  • Agreed metrics, a named pilot team, and a measurement method
2

Ground the AI

Deploy, collect baseline data, let it mature, refine. The first honest read on whether the KPI will move.

Leaves you with
  • A populated baseline for the chosen KPI
  • First signal readings and a working feedback loop
  • A go or no-go call made on data rather than opinion
3

Prove

Sprint 1 with the pilot teams, behaviour signals read, uplift measured against the baseline you built in step one.

Leaves you with
  • Measured uplift against baseline, not projected uplift
  • Unit economics: cost per user story and cost per associate
  • Demonstrated adoption, and a calibrated decision on scale

We build the baseline ourselves in step one, so uplift is measured rather than asserted. Most of the work is process and people. Technology is the smaller half.

Accelerated delivery · 4 to 6 weeks

For AI requests that cannot wait for a sequential discovery, design and build lifecycle. A Day 0 readiness check decides the path.

Four weeks

The problem boundary is reasonably defined, representative data is accessible, and the integration points are known. Straight into the operating loop.

Six weeks

The problem is still being framed, the data has not been located or cleared, or the systems landscape is unclear. Two to three weeks of real discovery first, then the same four-week engine.

Inside the four weeks - it takes roughly a week and a half to a working solution idea, a week and a half to two weeks of review, and a week to deploy, with about a week of float held across the plan for feedback and rework. Stages overlap rather than hand over. A working V1 passes a formal go or no-go gate before deeper refinement.

This is not a replacement for Agile. But the principles are the same. What differs is iteration intensity, how early a working harness or prototype or agent gets validated against real scenarios, and how fast business and technical decisions converge. Validation covers AI behaviour as well as function, which a standard acceptance criteria pass does not.

Contrarian positioning

The duality of AI & SI

AI companies build the model. Integrators connect the systems. We make AI work inside your business, on your data and in your workflows, and we stay with you after go-live.

Neither half is enough on its own

The industry puts specialist and integrator as an 'either or' choice. That framing is the problem. Specialist depth belongs in the work. Integrator accountability belongs in the result.

The AI specialist The integrator Where we stand
Strength Speed, novelty, model and agent skills. Trust, platform depth, continuity after go-live. Specialist skills, sold on integrator terms.
What is eroding it AI first has become a claim anyone can make. Analysts expect 70% of enterprises to abandon agentic AI built by vendor forward-deployed engineers by 2028. Platforms are absorbing configuration and deployment work, and build-and-run effort is being deflated at renewal. Integration is the stage being redefined and expanded. Cross-platform integration of embedded AI is a space most dont claim.
What the buyer worries about "Who maintains this when they leave?" "They will bill hours to configure what the vendor gives away." "Will it work on your data, in your systems, and keep working?"

Four layers, two different jobs

An AI programme passes through four layers on its way into a business. Two of them reward specialist depth, and two reward somebody being accountable for the result a year later.

SPECIALIST DEPTH

Data

An AI-ready, governed data foundation built on the platform you already own. This is the largest part of what we do.

SPECIALIST DEPTH

Systems and agents

Agents connected to systems of record across core systems, point solutions, product platforms with identity, permissions and audit carried through rather than bolted on.

INTEGRATOR ACCOUNTABILITY

Workflow and Process

Business processes redesigned around the agent. AI at the edges, rules at the core, humans for exceptions.

INTEGRATOR ACCOUNTABILITY

Control and continuity

Governance, evaluation and the run after go-live.

Which parts of IT services are actually shrinking?

What is ending is the billing of undifferentiated effort, and that has been ending for a decade. AI makes development cheap & faster. They make judgement, context and accountability more expensive, not less. The work moves up, from writing to deciding.

A honest take on the SI - AI duality would be as follows. The bottom of the integration market is compressing - routine application management, manual test execution, infrastructure administration. The top is expanding - business technology advisory, AI-led architecture, outcome-based co-creation. Firms that only ever sold the bottom have to pivot. The pricing basis has to change, but the service does not disappear.

The AI question and the SI question are the same question

Where does automation & intelligence belong in a multi platform estate that somebody has to keep running on Monday morning? Answering this means knowing the entire estate, not just owning the AI model.
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