AI consulting that ends in production
Strategy, architecture, and the data infrastructure underneath them. One team, accountable through deployment.
Strategy and implementation lose value every time the work changes hands
AI programmes pass through several hands before they reach production. Strategy is set in one room, architecture in another, and implementation somewhere else again, often at a different company. Each transition carries a cost, as context that was obvious during the workshop goes unwritten and constraints discovered during the build find no route back to the people who set the scope.
We call this the handoff tax, and it compounds in AI work because so much of the plan rests on conditions that only become visible once someone opens the data. A roadmap built without a clear view of data quality sets timelines that slip by week six. An architecture selected before anyone has established what training data can be sourced produces a model that underperforms in the language your customers use.
Our engagements hold the work in one place. The team that assesses your readiness designs the architecture, and the engineers who build the system were in the room when its success criteria were agreed.
Four workstreams, one accountable engagement
Readiness Assessment
Ten business days.
We audit your data estate, systems, and workflows to establish which AI applications are viable now, which become viable after remediation, and which do not fit the organization. The assessment produces a data inventory covering source systems, quality, lineage, and access constraints, alongside a use case register scored on value and feasibility. A technical gap analysis sets out the integration and capability work implementation depends on, with effort estimates attached so the sequencing decisions remain yours.
Strategy and Roadmap
Six weeks.
A sequenced adoption plan tied to specific business objectives, with dependencies mapped and owners named against every initiative. Budget models cover infrastructure, data, and staffing across the horizon of the plan, and a measurement framework defines what gets tracked, at what interval, and by whom.
Architecture and Solution Design
Four weeks.
We specify the system before anyone writes code, covering model selection, data pipeline design, integration with your existing estate, evaluation methodology, and deployment topology. Each significant choice is recorded in an architecture decision record with the alternatives considered, which leaves your team holding the reasoning as well as the conclusion. Evaluation is defined in the same pass, through a harness with acceptance thresholds agreed before the build begins.
Governance Framework
Four weeks, run in parallel.
Decision rights, model monitoring standards, risk assessment procedures, and audit trails, designed to hold up under scrutiny in every jurisdiction you operate in. The charter establishes who decides what, the model risk register sets assessment criteria and keeps them current, and the monitoring specifications cover drift detection and escalation. We hold ISO 27001 certification and design to the regimes that bind you, including GDPR, the EU AI Act, sector requirements in financial services and healthcare, and national data protection law in each market you serve.
Build, Buy, or Partner
Every initiative on the roadmap gets this decision made explicitly, with the reasoning written down.
We assess each capability against four criteria: whether it is a durable source of advantage or a commodity, what it costs to maintain once the build team moves on, whether the talent to operate it exists inside the organization, and what switching a vendor would cost in two years. Commodity capabilities are bought. Capabilities that carry your differentiation are built, and we say so even when the build runs through us. The output is a decision record per initiative, which is the document that survives a change of leadership.
Unit Economics
A model that performs well in evaluation can still be uneconomic in production.
We price the system before it is built. Tokens consumed per completed task, the context you resend on every call, retry and fallback rates, cache hit ratios, and the marginal cost of the ten thousandth user rather than the tenth. Those figures constrain model selection as heavily as benchmark performance does, and they are the difference between a system that scales and one whose only remaining lever is to degrade the product once the inference bill arrives.
Cost per resolved task therefore sits in the evaluation harness as an acceptance threshold, agreed alongside accuracy before the build begins. A system that clears one and fails the other has not passed.
Right-sizing follows from that discipline. A smaller model, given well-constructed retrieval and a task-specific head distilled from a larger one, frequently matches frontier performance on the narrow task you are paying for at a fraction of the token expenditure. Whether that holds for your workload is an empirical question, and we answer it against your data while the architecture is still open.
The Data Operation Behind the Advice
Our consulting practice sits on a working data operation. Our engineering and linguistics teams produce post-training data for frontier and enterprise models, covering expert trajectory data, step-level reasoning verification, and reinforcement learning from human feedback for code. The same teams build annotation pipelines with defined quality gates, evaluation harnesses that hold models to thresholds agreed in advance, and speech and text corpora for languages no commercial vendor supplies. Our open datasets, mGhana-ST and UGSpeechData, are published on Hugging Face and drew more than 12,000 downloads in March 2026 alone.
This changes what a recommendation costs to act on. When an architecture calls for evaluation data for a code model, a corpus in a language with no commercial source, or an annotation pipeline your own team will operate, specification and delivery run under one engagement. The people who set the thresholds are the ones who have to meet them.
It also disciplines the advice. Our recommendations are shaped by capacity we can account for, and when implementation surfaces something the assessment missed, the correction is a conversation between colleagues instead of a change order.
Where the Data Runs Out
Enterprise AI is straightforward where data is abundant, documented, and in English. The harder problems sit elsewhere: languages with no commercial corpus, records that were never digitized, regulatory environments with no settled precedent for automated decisions, and infrastructure that cannot assume reliable connectivity.
We built the practice on those conditions. Our African language datasets exist because the commercial market did not supply them, and the methods behind them transfer to any low-resource environment. For clients in Europe and North America, that work shows up as capability in the parts of their estate the market has not solved on their behalf.
Four phases from assessment to implementation oversight
Discovery and Assessment Workshops with your leadership and technical teams alongside an audit of systems and data, through which we establish current state and agree the success metrics the engagement is measured against.
Strategy and Roadmap Initiatives sequenced by value and dependency, with resourcing defined against each. The output is a plan leadership can approve and engineering can execute without translation.
Solution Design Architecture, pipelines, integration, and governance specified in full, with acceptance thresholds set before the build begins.
Implementation and Oversight We stay engaged through deployment, measuring performance against the metrics agreed in Phase 01 and revising the roadmap as production reveals what an assessment cannot.
The teams that benefit most from this engagement model
Enterprise leadership that needs an AI investment case rigorous enough to defend to a board and specific enough to execute against.
Teams before their first deployment that know AI matters and need to establish where to begin.
Teams with a pilot that will not scale, where the distance between a working experiment and a production system is infrastructure, governance, and change management.
Regulated institutions in financial services, healthcare, and government, where governance is a condition of deployment and has to sit inside the first architecture decision.
Frequently asked questions
An AI readiness assessment is a structured audit of an organization's data infrastructure, technical capability, and operational workflows, conducted to establish which AI applications are viable and what remediation implementation depends on. AdwumaTech delivers readiness assessments in ten business days, producing a data inventory, a scored use case register, a technical gap analysis, and a sequenced remediation plan.
Scope determines timeline. A readiness assessment runs ten business days, a full adoption roadmap six weeks, and architecture and solution design a further four weeks. Implementation oversight is continuous and scales with deployment complexity.
Both. Our engineering and linguistics teams in Accra build the datasets, pipelines, and evaluation infrastructure our consultants specify, so the engagement continues through delivery and into post-deployment oversight.
We assess each initiative on four criteria: whether the capability is a durable source of advantage or a commodity, its maintenance cost once the build team moves on, whether the talent to operate it exists in the organization, and the cost of switching vendors in two years. The reasoning is recorded per initiative.
We model token expenditure per completed task, resent context, retry and fallback rates, cache hit ratios, and the marginal cost of scale, then set cost per resolved task as an acceptance threshold in the evaluation harness alongside accuracy. Model selection follows from both figures, which frequently favours a smaller right-sized model over a frontier model on the specific task being paid for.
A governance charter defining decision authority, a model risk register with assessment criteria, monitoring and drift detection standards, incident response procedures, and audit-structured documentation, designed to the regulatory regime the organization operates under.
Our deployment experience spans financial services, the public sector, agriculture, and energy, across Africa, North America, and Europe. In financial services that has meant identity verification, synthetic identity and deepfake detection, and compliance workloads where a model decision has to be explainable to a regulator. In the public sector it has meant national-scale systems the institution has to own and operate after handover, which places different demands on documentation and knowledge transfer than a commercial deployment does. In agriculture it has meant predictive models built on sparse, seasonal, and unevenly recorded field data. In energy it has meant operational forecasting and asset workloads. We have also delivered engagements in healthcare and construction.
Most begin with a readiness assessment. Schedule a consultation.