Industrial AI, robotics conversion and adoption, Industry 4.0 automation and test automation, and the software those systems run inside: enterprise systems (ERP, HRMS, CRM, inventory and production management), web applications, AI agents and automation, and native mobile apps for iOS and Android.
A focused first release typically ships in 8–12 weeks. Multi-module enterprise platforms run 4–9 months, delivered module by module so you are using working software long before the final release rather than waiting on a single big-bang launch.
Both. A well-defined scope gets a fixed price against a signed specification. Longer or evolving builds run as a dedicated team on a monthly retainer. Either way you get a written estimate with every assumption listed before a contract exists, so you can see exactly what moves the number.
Discovery and scoping, then UI/UX design, then two-week build sprints that each end in a working demo. You get a staging environment from the first week, and QA, security review and deployment run inside the sprint instead of being bolted on at the end.
You do, in full, on final payment. Source code, design files, infrastructure and documentation are handed over in your own repositories and cloud accounts. We keep no licence over your product and there is no lock-in to our hosting.
Yes. We connect to MES and the equipment already on the line, accounting, HR and CRM platforms, payment gateways, WhatsApp Business, and legacy in-house databases. Where no API exists we build a sync layer and migrate historical records with reconciliation reports your finance team can check line by line.
Every project closes with a handover: documentation, team training, and a warranty period covering defects at no cost. After that you can run support in-house or keep us on a monthly plan covering monitoring, security patches, and an agreed block of enhancement hours.
We sign your NDA before the first technical conversation. Builds ship with role-based access, encryption in transit and at rest, and audit logging as standard. Production data stays inside your infrastructure and access is limited to the engineers assigned to your project.
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Tools
The stack we build on
We design, build, and evaluate with a modern AI stack—LLMs, vector search, orchestration, and observability—so your features are fast, reliable, and secure.
Choosing a model is a decision you will make again. Build so that it is cheap to make again.
Every AI programme opens with the same question, and it is the question with the shortest shelf life in the whole project. The models available a year from now will not be the ones you evaluated. The useful work is not picking the winner today; it is putting the model behind a boundary so that next year's choice costs an afternoon rather than a quarter. This is how to choose, on which axes, and how to keep the choice reversible.
A model is not better or worse in the abstract. It is better or worse at your task, at your volume, under your latency budget, inside your data boundary, for as long as it stays available. These are the five things worth measuring before anything is signed.
01
Capability on your task
Task-specific accuracy measured against a held-out set of your own examples, not a public leaderboard position
The only capability question that matters is whether the model does your job well, and the only way to answer it is to assemble a set of real cases from your business, with the correct answer written down beside each one, and run every candidate against it. Public rankings measure general ability on general problems. Your problem is specific: your vocabulary, your document formats, your edge cases, your definition of a correct answer. Models that rank close together in general very often separate sharply on a narrow task, and the separation goes in directions nobody predicts. Building that set of cases is the single most useful week of work in a model decision, and it keeps paying out, because it is the same set you use to check the next model, and the one after that.
You choose on reputation, discover the gap in production, and have no way of telling whether the replacement is better or merely different, because nothing was ever measured in the first place.
02
Latency
Time to first token, generation speed, and total response time measured under real concurrency rather than in isolation
Latency is the delay between asking and being answered. It has two parts that behave differently: how long you wait before anything at all appears, and how quickly the answer then arrives word by word. Which part matters depends entirely on where the model sits. A person waiting at a screen forgives a slow full answer if text starts moving quickly, and abandons the tool if the first second is silent. A background job that reconciles overnight does not care about either. An agent that calls a model eight times in a chain multiplies every millisecond by eight, which is where teams are most often surprised. Larger models are generally slower, so latency and capability pull against each other on almost every decision.
You pick on quality alone, ship something correct and unusably slow, and then spend the following month re-engineering around a constraint that was measurable on day one.
03
Cost per call at your volume
Unit cost multiplied by real traffic, including retries, tool loops, and everything the system prompt carries on every single call
A per-call price looks negligible in a demonstration and decides the business case at scale. The multiplier is what people miss. Every call carries your instructions, your retrieved context and your output format, whether or not the user's question needed them. Agents that reason across several steps pay that toll on each step. Retries after a failure pay it again. The honest arithmetic is unit cost times realistic monthly volume times the number of model calls a single piece of work actually triggers, and that last number is usually larger than anyone expects. This is also where a smaller model earns its place: if it clears your accuracy bar on this specific task, the saving at volume is not marginal.
The pilot is affordable and the rollout is not. The programme is then cancelled for being too expensive, when what was too expensive was one avoidable choice made before anyone counted.
04
Where it runs
A hosted API, a model deployed inside your own cloud tenancy, or self-hosted inference on hardware you operate
There are three places a model can run, and the difference is mostly about where your data goes and who carries the operational load. A hosted API means the supplier runs the model and your text travels to them. Deploying inside your own cloud tenancy means the weights run in infrastructure you already control, under your existing agreements. Self-hosted means you run inference yourself, on your own machines: inference is the act of running a trained model to produce an answer, as distinct from training it in the first place. The right answer is usually dictated by regulation, contract or client commitment rather than by preference, so it is worth settling early, because it constrains everything downstream.
You build against a hosted API, then meet a client contract or a regulator that forbids the data leaving your estate, and discover the constraint after the architecture has set around it.
05
Longevity and deprecation risk
Version lifecycles, notice periods before retirement, and how much of your system's behaviour is pinned to one specific set of weights
Deprecation is when a supplier retires a model version and stops serving it, usually with notice and usually on their schedule rather than yours. It is not an edge case; it is the normal life of a hosted model, and any system running long enough will meet it. The exposure is not the migration itself but everything tuned around the old model's particular behaviour: prompts written against its habits, output parsing shaped to its formatting, thresholds calibrated to its confidence. Open-weight models, where the weights are published and you may run them yourself, invert this: nobody can retire a file you hold, though nobody maintains it for you either.
A dated notice arrives, and the migration turns out to touch dozens of prompts and every downstream parser, because the model was never a component, it was assumed everywhere.
Two families
Hosted frontier models and open-weight models, compared without a thumb on the scale.
Frontier here means the largest, most capable class available at any given moment, served by a supplier over an API. Open-weight means the trained parameters are published, so you may download them and run inference on your own infrastructure. Both are legitimate answers to different questions.
Hosted frontier model
Open-weight, self-hosted
Capability ceiling
The highest generally available, and it rises without you doing anything. Hardest reasoning, longest chains, most reliable instruction following
Below the frontier at any given moment, though the gap narrows continually and is often irrelevant on a narrow, well-defined task
Cost shape
Pure variable cost. Nothing at rest, everything per call. Cheap to start, and it scales linearly with success
Mostly fixed cost. Hardware or reserved capacity whether or not anyone calls it. Poor economics at low volume, strong at sustained high volume
Latency control
You get what the supplier serves, including their variance under global load, and you cannot tune it
Yours to engineer. Batching, quantisation, dedicated hardware. Quantisation stores the weights at lower numerical precision, trading a little accuracy for speed and memory
Data boundary
Your text leaves your estate. Manageable through contracts and regional deployment, but it is a boundary crossing and some contracts forbid it
Nothing leaves. This is the reason most self-hosting decisions are actually made, and it is frequently decisive on its own
Operating burden
Close to none. The supplier handles serving, scaling, availability and hardware entirely
Real and permanent. Serving infrastructure, capacity planning, upgrades, monitoring and someone on call who understands inference
When it is the right call
Early work where the task is not yet pinned down, genuinely hard reasoning, spiky or uncertain traffic, and small teams without infrastructure people to spare
A hard data boundary, sustained high volume on a narrow task, or a requirement that the system keep working unchanged for years regardless of any supplier's roadmap
In shortMost systems should start hosted, because at the beginning you do not yet know what the task requires and paying per call to find out is the cheapest research available. Self-hosting an open-weight model becomes the right answer when a boundary makes it mandatory, or when volume on a settled task makes fixed cost cheaper than variable cost. Neither family is the mature choice. Committing to either one before you have measured your own task is the immature choice.
How to choose
Start from your constraint, not from the model.
The binding constraint is almost never capability. It is a data boundary, a latency budget, or a volume that makes the arithmetic decide for you. Find which one binds first, and the branch follows.
Read each branch as its condition first, then its outcome. Take the first condition that genuinely applies to you and stop there, because the constraints are in order of how hard they are to argue with: a data boundary is not negotiable, a latency budget rarely is, and cost only decides once the first two are satisfied. If none of them binds, the top branch is correct and remains correct until something changes.
The reflex worth resisting
Sometimes the bigger model is the answer. More often it is an expensive habit.
Reaching for the largest available model is the default, because it is the choice nobody is criticised for making. It is right when the task is genuinely hard, and it quietly wastes money and seconds on every call when the task is not.
A bigger model is the answer when
The task needs multi-step reasoning where an error early in the chain invalidates everything after it
Instructions are long, conditional and interlocking, and smaller models start dropping clauses under the weight of them
The work is open-ended rather than templated, and no two inputs look much alike
You are still exploring what is possible, and paying more per call to avoid mistaking a small model's limits for the task being impossible
The volume is genuinely low and each answer is genuinely valuable, so unit cost is not the number that decides anything
A smaller model is the answer when
The task is classification, extraction, routing or reformatting, where the shape of the answer is fixed and known in advance
You have measured both on your own held-out set and the smaller one clears your accuracy bar, which happens far more often than teams expect
The volume is high enough that per-call cost has become a line somebody reviews
Someone is waiting at a screen, and the smaller model answers fast enough to feel immediate rather than merely acceptable
The model sits inside an agent loop that calls it repeatedly, where every difference in speed and cost is multiplied by the number of steps
Asked often
The questions that come up once the decision is real.
How do we compare models on our own task rather than on general rankings?
Collect a set of real cases from your business, ideally at least a hundred, weighted towards the awkward ones rather than the easy ones, and write down the correct answer for each. Define what scoring means before you run anything, because deciding afterwards is how a preferred model wins. Then run every candidate through the same set with the same prompt and compare on accuracy, latency and cost together rather than on accuracy alone. The set outlives the decision: it is what you use to check the next model, and to prove that a change made things better rather than merely different.
What happens when a model is deprecated?
Deprecation means the supplier retires a version and stops serving it, normally with a notice period. If the model sits behind a boundary in your code and you hold an evaluation set, migration is a configuration change, a run of the evaluation, a look at whatever regressed, and a deploy. If it does not, the same event means auditing every prompt, every output parser and every threshold that was quietly tuned to the old model's habits. The event is identical in both cases. The difference in cost is entirely a matter of how the system was built.
Can we switch models without a rewrite?
Yes, if the system was built for it, and that means treating the model as a replaceable component from the first week. One place in the code that calls a model, with everything above it working through your own interface. Prompts stored as versioned assets rather than scattered through the application. Output validated against a schema you own, so the rest of the system depends on your contract and not on the model's formatting habits. An evaluation set that can be run against a candidate on demand. None of that is expensive at the start, and all of it is expensive to retrofit under a deadline.
Does open-weight mean free?
It means the weights are published and you may run them, not that running them costs nothing. You are exchanging a variable per-call bill for a fixed one: hardware or reserved capacity that is paid for whether or not anyone uses it, plus serving infrastructure, capacity planning, upgrades, monitoring, and people who understand inference well enough to be called at night. At sustained high volume on a settled task, that trade is often clearly favourable. At low or uneven volume it is usually worse than the bill it replaced, and the cost simply moved from a supplier's invoice to your own payroll.
Should we use more than one model in the same system?
Frequently, and it is the natural consequence of taking the axes seriously. Different steps have different requirements: a fast small model for classification and routing, a larger one for the step that genuinely requires reasoning, a self-hosted one for the path that touches data which may not leave the estate. The condition is that the same discipline applies to each. Multiple models behind one boundary, each with its own evaluation set, is good engineering. Multiple models called directly from wherever somebody happened to need one is the same problem you started with, multiplied.
In practice
Make the model swappable. Then the choice stops being frightening.
Almost every anxious version of this question assumes the decision is permanent, and it is that assumption, rather than the decision itself, that causes the damage. Teams spend months comparing models they will not be using in a year, and spend nothing on the boundary that would have made the comparison repeatable. The choice is not architecture. It should live in configuration, behind one interface, with prompts held as versioned assets, outputs validated against a schema you own, and an evaluation set built from your own work that can be pointed at any candidate on an afternoon's notice.
Build it that way and the landscape moving faster than your procurement cycle stops being a threat and becomes something you can simply take advantage of. A better, cheaper or faster model appears; you run your set; you see the numbers; you change a line, or you decide not to. That is the whole event. If you would rather not work out where that boundary belongs in your own system alone, it is a short conversation, and we would rather help you build something you can change than sell you a model you cannot.
OpenAI
Anthropic
Gemini
xAI
Hugging Face
Kimi
LangChain
NVIDIA
Pinecone
Databricks
